IMAGE: a multivariate multi-site stochastic weather generator for European weather and climate

Capturing the spatial and temporal correlation of multiple variables in a weather generator is challenging. A new massively multi-site, multivariate daily stochastic weather generator called IMAGE is presented here. It models temperature and precipitation variables as latent Gaussian variables with temporal behaviour governed by an auto-regressive model whose residuals and parameters are correlated through resampling of principle component time series of empirical orthogonal function modes. A case study using European climate data demonstrates the model’s ability to reproduce extreme events of temperature and precipitation. The ability to capture the spatial and temporal extent of extremes using a modified Climate Extremes Index is demonstrated. Importantly, the model generates events covering not observed temporal and spatial scales giving new insights for risk management purposes.     

Implementing quality control on a random number stream to improve a stochastic weather generator

For decades, stochastic modellers have used computerized random number generators to produce random numeric sequences fitting a specified statistical distribution. Unfortunately, none of the random number generators we tested satisfactorily produced the target distribution. The result is generated distributions whose mean even diverges from the mean used to generate them, regardless of the length of run. Non‐uniform distributions from short sequences of random numbers are a major problem in stochastic climate generation, because truly uniform distributions are required to produce the intended climate parameter distributions. In order to ensure generation of a representative climate with the stochastic weather generator CLIGEN within a 30‐year run, we tested the climate output resulting from various random number generators. The resulting distributions of climate parameters showed significant departures from the target distributions in all cases. We traced this failure back to the uniform random number generators themselves. This paper proposes a quality control approach to select only those numbers that conform to the expected distribution being retained for subsequent use. The approach is based on goodness‐of‐fit analysis applied to the random numbers generated. Normally distributed deviates are further tested with confidence interval tests on their means and standard deviations. The positive effect of the new approach on the climate characteristics generated and the subsequent deterministic process‐based hydrology and soil erosion modelling are illustrated for four climatologically diverse sites. Copyright © 2007 John Wiley & Sons, Ltd.     

Eddy-permitting simulations of the sub-polar North Atlantic: impact of the model bias on water mass properties and circulation

Some previous studies demonstrated that model bias has a strong impact on the quality of long-term prognostic model simulations of the sub-polar North Atlantic Ocean. Relatively strong bias of water mass characteristics is observed in both eddy-permitting and eddy-resolving simulations, suggesting that an increase of model resolution does not reduce significantly the model bias. This study is an attempt to quantify the impact of model bias on the simulated water mass and circulation characteristics in an eddy-permitting model of the sub-polar ocean. This is done through comparison of eddy-permitting prognostic model simulations with the results from two other runs in which the bias is constrained by using spectral nudging. In the first run, the temperature and salinity are nudged towards climatology in the whole column. In the second run, the spectral nudging is applied in the surface 30 m layer and at depths below 560 m only. The biases of the model characteristics of the unconstrained run are similar to those reported in previous eddy-permitting and eddy-resolving studies. The salinity in the surface and intermediate waters of the Labrador Sea waters increases with respect to the climatology, which reduces the stability of the water column. The deep convection in the unconstrained run is artificially intensified and the transport in the sub-polar gyre stronger than in the observations. In particular, the transport of relatively salty and warm Irminger waters into the Labrador Sea is unrealistically high. While the water mass temperature and salinity in the run with spectral nudging in the whole column are closest to the observations, the depth of the winter convection is underestimated in the model. The water mass characteristics and water transport in the run with spectral nudging in the surface and deep layers only are close to observations and at the same time represent well the deep convection in terms of its intensity and position. The source of the bias in the prognostic model run is discussed.     

Assessment of the effectiveness of a multi-site stochastic weather generator on hydrological modelling in the Red Deer River watershed,Canada

ABSTRACT

To improve the convergence of multiple-site weather generators (SWGs) based on the brute force algorithm (MBFA), a genetic algorithm (GA) is proposed to search the overall optimal correlation matrix. Precipitation series from weather generators are used as input to the hydrological model, the soil and water assessment tool (SWAT), to generate runoff over the Red Deer watershed, Canada for further runoff analysis. The results indicate that the SWAT model using SWG-generated data accurately represents the mean monthly streamflow for most of the months. The multi-site generators were capable of better representing the monthly streamflow variability, which was notably underestimated by the single-site version. In terms of extreme flows, the proposed method reproduced the observed extreme flow with smaller bias than MBFA, while the single-site generator significantly underestimated the annual maximum flows due to its poor capability in addressing partial precipitation correlations.     

Simulation of future climate scenarios with a weather generator

Numerous studies across multiple disciplines search for insights on the effects of climate change at local spatial scales and at fine time resolutions. This study presents an overall methodology of using a weather generator for downscaling an ensemble of climate model outputs. The downscaled predictions can explicitly include climate model uncertainty, which offers valuable information for making probabilistic inferences about climate impacts. The hourly weather generator that serves as the downscaling tool is briefly presented. The generator is designed to reproduce a set of meteorological variables that can serve as input to hydrological, ecological, geomorphological, and agricultural models. The generator is capable of reproducing a wide set of climate statistics over a range of temporal scales, from extremes, to low-frequency interannual variability; its performance for many climate variables and their statistics over different aggregation periods is highly satisfactory. The use of the weather generator in simulations of future climate scenarios, as inferred from climate models, is described in detail. Using a previously developed methodology based on a Bayesian approach, the stochastic downscaling procedure derives the frequency distribution functions of factors of change for several climate statistics from a multi-model ensemble of outputs of General Circulation Models. The factors of change are subsequently applied to the statistics derived from observations to re-evaluate the parameters of the weather generator. Using embedded causal and statistical relationships, the generator simulates future realizations of climate for a specific point location at the hourly scale. Uncertainties present in the climate model realizations and the multi-model ensemble predictions are discussed. An application of the weather generator in reproducing present (1961-2000) and forecasting future (2081-2100) climate conditions is illustrated for the location of Tucson (AZ). The stochastic downscaling is carried out using simulations of eight General Circulation Models adopted in the IPCC 4AR, A1B emission scenario.     

Parameterization of synoptic weather systems in the South Atlantic Bight for modeling applications

An event based, long-term, climatological analysis is presented that allows the creation of coastal ocean atmospheric forcing on the coastal ocean that preserves both frequency of occurrence and event time history. An algorithm is developed that identifies individual storm event (cold fronts, warm fronts, and tropical storms) from meteorological records. The algorithm has been applied to a location along the South Atlantic Bight, off South Carolina, an area prone to cyclogenesis occurrence and passages of atmospheric fronts. Comparison against daily weather maps confirms that the algorithm is efficient in identifying cold fronts and warm fronts, while the identification of tropical storms is less successful. The average state of the storm events and their variability are represented by the temporal evolution of atmospheric pressure, air temperature, wind velocity, and wave directional spectral energy. The use of uncorrected algorithm-detected events provides climatologies that show a little deviation from those derived using corrected events. The effectiveness of this analysis method is further verified by numerically simulating the wave conditions driven by the characteristic wind forcing and comparing the results with the wave climatology that corresponds to each storm type. A high level of consistency found in the comparison indicates that this analysis method can be used for accurately characterizing event-based oceanic processes and long-term storm-induced morphodynamic processes on wind-dominated coasts.     

Relationship between zonal position of the North Atlantic Oscillation and Euro-Atlantic blocking events and its possible effect on the weather over Europe

The North Atlantic Oscillation(NAO) exhibited a marked eastward shift in the mid-1970 s. Observations show that the extreme weather events in Europe have emerged frequently in the past decades. In this paper, based upon the daily NAO index, we have calculated the frequency of in-situ NAO events in winter during 1950-2011 by defining the Eastern-type NAO(ENAO) and Western-type NAO(WNAO) events according to its position at the east(west) of 10°W. Then, the composites of the blocking frequency, temperature and precipitation anomalies for different types of NAO events are performed. Results show that the frequency of Euro-Atlantic blocking events is distributed along the northwest-southeast(southwest-northeast) direction for the negative(positive) phase. Two blocking action centers in Greenland and European continent are observed during the negative phase while one blocking action center over south Europe is seen for the positive phase. The action center of blocking events tends to shift eastward as the NAO is shifted toward the European continent. Moreover, the eastern-type negative phase(ENAO) events are followed by a sharp decline of surface air temperature over Europe(especially in central, east, and south Europe), which have a wider and stronger impact on the weather over European continent than the western-type negative phase(WNAO) events do. A double- branched structure of positive precipitation anomalies is seen for the negative phase event, besides strong positive precipitation anomalies over south Europe for ENAO event. The eastern-type and western-type positive phase(ENAO+ and WNAO+) can lead to warming over Europe. A single-branched positive precipitation anomaly dominant in central and north Europe is seen for positive phase events.     

Plastic ingestion in the North Atlantic fulmar

Fulmars found dead on the Dutch coast, and fulmars collected in arctic colonies have considerable quantities of plastic in their stomachs. The average number of plastic items ingested is almost twelve in Dutch fulmars, and four to five in arctic fulmars. User-plastics and industrial plastics are about equally abundant. Ingestion of user-plastics suggests a stronger impact of toxic chemicals from plastics than generally assumed.     

Geomagnetic activity and the North Atlantic Oscillation

The North Atlantic Oscillation (NAO) is the prominent pattern of winter climate variability that has a strong effect on weather in the North Atlantic region and the adjacent continents. At present, uncertainty prevails as to the mechanisms controlling the variability of the NAO. It is also difficult to explain why the positive phase of the NAO has prevailed over the past 37 years (1972–2008). We found high positive correlation coefficients between geomagnetic activity (used as a measure of solar wind intensity) and the NAO indices that equal 0.76 for 1962–1994 and 0.63 for 1961–2011. Positive correlations of the distribution of surface air temperature with the NAO and similarly with geomagnetic activity occur in the Northern Hemisphere. These results encourage our search for possible causes controlling the NAO. We have found that at times of high geomagnetic activity the NAO index is positive and magnetic reconnection may enable the solar wind to initiate downward winds in the magnetosphere. Wind anomalies originate at the edge of the stratospheric polar vortex and propagate downward through the troposphere taking part in the intensification of the vortex and of the westerlies. Stronger northerly winds over Greenland carry cold air southward and, together with the enhanced westerlies, advect the warm air from the Atlantic along the deep Icelandic low into Eurasia increasing temperatures there. On the other hand, at times of low geomagnetic activity, the NAO index is negative and the stratospheric polar vortex is weak. Warm air from the subtropics is carried into the Arctic and a rapid amplification of planetary waves propagating upward may cause displacement or even splitting of the weak vortex and sudden stratospheric warming. During this negative NAO phase the weakened westerlies allow more cold air to build up over North America and Eurasia.     

Using a rainfall stochastic generator to detect trends in extreme rainfall

An original approach is proposed to estimate the impacts of climate change on extreme events using an hourly rainfall stochastic generator. The considered generator relies on three parameters. These parameters are estimated by average, not by extreme, values of daily climatic characteristics. Since climate changes should result in parameters instability in time, the paper focuses on testing the presence of linear trends in the generator parameters. Maximum likelihood tests are used under a Poisson–Pareto-Peak-Over-Threshold model. A general regionalization procedure is also proposed which offers the possibility to work on both local and regional scales. From the daily information of 139 rain gauge stations between 1960 and 2003, changes in heavy precipitations in France and their impacts on quantile predictions are investigated. It appears that significant changes occur mainly between December and May for the rainfall occurrence which increased during the four last decades, except in the Mediterranean area. Using the trend estimates, one can deduced that these changes, up to now, do not affect quantile estimations.     

Prediction model for spring dust weather frequency in North China

It is of great social and scientific importance and also very difficult to make reliable prediction for dust weather frequency (DWF) in North China. In this paper, the correlation between spring DWF in Beijing and Tianjin observation stations, taken as examples in North China, and seasonally averaged surface air temperature, precipitation, Arctic Oscillation, Antarctic Oscillation, South Oscillation, near surface meridional wind and Eurasian westerly index is respectively calculated so as to construct a prediction model for spring DWF in North China by using these climatic factors. Two prediction models, i.e. model-I and model-II, are then set up respectively based on observed climate data and the 32-year (1970 -2001) extra-seasonal hindcast experiment data as reproduced by the nine-level Atmospheric General Circulation Model developed at the Institute of Atmospheric Physics (IAP9L-AGCM). It is indicated that the correlation coefficient between the observed and predicted DWF reaches 0.933 in the model-I, suggesting a high prediction skill one season ahead. The corresponding value is high up to 0.948 for the subsequent model-II, which involves synchronous spring climate data reproduced by the IAP9L-AGCM relative to the model-I. The model-II can not only make more precise prediction but also can bring forward the lead time of real-time prediction from the model-I’s one season to half year. At last, the real-time predictability of the two models is evaluated. It follows that both the models display high prediction skill for both the interannual variation and linear trend of spring DWF in North China, and each is also featured by different advantages. As for the model-II, the prediction skill is much higher than that of original approach by use of the IAP9L-AGCM alone. Therefore, the prediction idea put forward here should be popularized in other regions in China where dust weather occurs frequently.     

The density of North Atlantic and North Pacific deep waters

The densities of seventeen samples of seawater from GEOSECS stations 27 (North Atlantic) and 217 (North Pacific) have been measured with a vibrating flow densimeter at 25°C. The densities of the deep samples were found to be 5 ± 1.5and16 ± 3.6ppm greater, for the North Atlantic and North Pacific, respectively, than predicted by the equation of state of Millero, Gonzalez and Ward (1976) derived for seawaters of constant relative composition. The results are in good agreement with the density anomalies predicted by Brewer and Bradshaw (1975) on the basis of the observed increase of dissolved silica, alkalinity and total carbon dioxide in oceanic deep waters. The application of these corrections results in an agreement with the Millero, Gonzalez and Ward (1976) equation of state to ±4 ppm.     

Generating probabilistic estimates of hydrological response for Irish catchments using a weather generator and probabilistic climate change scenarios

In accounting for uncertainties in future simulations of hydrological response of a catchment, two approaches have come to the fore: deterministic scenario‐based approaches and stochastic probabilistic approaches. As scenario‐based approaches result in a wide range of outcomes, the role of probabilistic‐based estimates of climate change impacts for policy formulation has been increasingly advocated by researchers and policy makers. This study evaluates the impact of climate change on seasonal river flows by propagating daily climate time series, derived from probabilistic‐based climate scenarios using a weather generator (WGEN), through a set of conceptual hydrological models. Probabilistic scenarios are generated using two different techniques. The first technique used probabilistic climate scenarios developed from statistically downscaled scenarios for Ireland, hereafter called SDprob. The second technique used output from 17 global climate models (GCMs), all of which participated in CMIP3, to generate change factors (hereafter called CF). Outputs from both the SDprob and the CF approach were then used in combination with WGEN to generate daily climate scenarios for use in the hydrological models. The range of simulated flow derived with the CF method is in general larger than those estimated with the SDprob method in winter and vice versa because of the strong seasonality in the precipitation signal for the 17 GCMs. Despite this, the simulated probability density function of seasonal mean streamflow estimated with both methods is similar. This indicates the usefulness of the SDprob or probabilistic approach derived from regional scenarios compared with the CF method that relies on sampling a diversity of response from the GCMs. Irrespective of technique used, the probability density functions of seasonal mean flow produced for four selected basins is wide indicating considerable modelling uncertainties. Such a finding has important implications for developing adaptation strategies at the catchment level in Ireland. Copyright © 2011 John Wiley & Sons, Ltd.     

Tar ‘specks’ in the North Atlantic

A number of dark specks retained on filters, through which seawater had been filtered, contained material that absorbed UV light very strongly. This material was most likely sunken pieces of tar. The amount was 0.12 μg/l on an average and was independent of hydrographic parameters.     

Late Cretaceous magnetic anomalies in the North Atlantic

An identification of anomalies 31–34 is presented for the North Atlantic. North of the Azores-Gibraltar Ridge this implies a revision of the identification of the magnetic anomalies older than anomaly 26. DSDP site 10 in the western North Atlantic appears to be located on the old end of anomaly 33. The relative spacings of anomalies 29–34 in the North and South Atlantic, North and South Pacific and Indian Oceans are compared and the estimated relative widths of the magnetic polarity intervals in the Late Cretaceous are revised.