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Solar hydrogen production by coupling of pressurized high temperature electrolyser with concentrated solar tower technology is studied. As the high temperature electrolyser requires constant temperature conditions, the focus is made on a molten salt solar tower due to its high storage capacity. A flowsheet was developed and simulations were carried out with Aspen Plus 8.4 software for MW-scale hydrogen production plants. The solar part was laid out with HFLCAL software. Two different scenarios were considered: the first concerns the production of 400 kg/d hydrogen corresponding to mobility use (fuel station). The second scenario deals with the production of 4000 kg/d hydrogen for industrial use. The process was analyzed from a thermodynamic point of view by calculating the overall process efficiency and determining the annual production. It was assumed that a fixed hydrogen demand exists in the two cases and it was assessed to which extent this can be supplied by the solar high temperature electrolysis process including thermal storage as well as hydrogen storage. For time periods with a potential over supply of hydrogen, it was considered that the excess energy is sold as electricity to the grid. For time periods where the hydrogen demand cannot be fully supplied, electricity consumption from the grid was considered. It was assessed which solar multiple is appropriate to achieve low consumption of grid electricity and low excess energy. It is shown that the consumption of grid electricity is reduced for increasing solar multiple but the efficiency is also reduced. At a solar multiple of 3.0 an annual solar-to-H2 efficiency greater than 14% is achieved at grid electricity production below 5% for the industrial case (4000 kg/d). In a sensitivity study the paramount importance of electrolyser performance, i.e. efficiency and conversion, is shown.

We investigate whether the recently observed temperature and precipitation trends over the Baltic Sea Basin are consistent with state-of-the-art regional climate model projections. To address this question we use several data sources: 1) multi-decadal trends derived from various observational data sets, 2) estimates of natural variability provided by a 2,000-year paleoclimatic model simulation, and 3) response to greenhouse gas forcing derived from regional climate simulations driven by the A1B and RCP4.5 scenarios (from ENSEMBLES and CORDEX projects). Results indicate that, over the past decades, the climate in the Baltic Sea Basin has undergone a change that is beyond the estimated range of natural variability. We test the hypothesis that this change may be understood as a manifestation of global warming due to increasing concentrations of greenhouse gases (GHGs). We find that changes in near-surface temperature support our hypothesis that the effect of GHG is needed to simulate the observed changes. The pattern correlation and regression results clearly illustrate the concerted emergence of an anthropogenic signal consistent with the GHG signal in summer and autumn in the 21st century. However, none of the 19 regional climate simulations used in this study reproduce the observed warming. The observed trends in precipitation and surface solar radiation are also partially inconsistent with the expected changes due to GHG forcing. We conclude that, besides the regional response to GHG forcing, other human-made drivers have had an imprint. Regional emission of industrial aerosols has been strongly reduced in this region, and we suggest that this reduction may be the missing driver.

The dependency of renewable energy systems on environmental influences such as sun or wind availability is one of the greatest challenges in the energy transition. For this purpose, it is important to develop electro chemical storage systems for long-term storing of renewable electricity in the form of hydrogen or methane. In the presented work, an electrolysis system with solid-oxide electrolyser stacks is designed. A solar thermal receiver is used to produce the steam supplied to the electrolyser stacks. On the other hand, a thermal energy storage, using a phase change material, is used for the extension of the operational hours during the night time. The system is optimized to minimize the levelized costs of hydrogen and compared to a system without a thermal energy storage. Finally, the cost sensitivity for the four main components of the cost structure is evaluated and discussed.

Annual and seasonal statistics of local air pressure characteristics have already been used as proxies for storminess across Northern Europe. We present an update of such proxies for Northern Europe and an unprecedented analysis for Central Europe which together considerably extends the current knowledge of European storminess. Calculations are completed for three sets of stations, located in North-Western, Northern and Central Europe. Results derived from spatial differences (geo- strophic winds) and single station pressure changes per 24 h support each other. Geostrophic winds' high percentiles (95th, 99th) were relatively high during the late nineteenth and the early twentieth century; after that they leveled off somewhat, to get larger again in the late twentieth century. The decrease happens suddenly in Central Europe and over several decades in Northern Europe. The subsequent rise is most pronounced in North-Western Europe, while slow and steady in Central Europe. Europe's storm climate has undergone significant changes throughout the past 130 years and comprises significant variations on a quasi- decadal timescale. Most recent years feature average or calm conditions, supporting claims raised in earlier studies with new evidence. Aside from some dissimilarity, a gen- eral agreement between the investigated regions appears to be the most prominent feature. The capability of the NAO index to explain storminess across Europe varies in space and with the considered period.

This study examines the performance of the analog method for downscaling daily precipitation. The evaluation is performed for (1) a number of similarity measures for searching analogs, (2) various ways to include the past atmospheric evolution, and (3) different truncations in EOF space. It is carried out for two regions with complex topographic structures, and with distinct climatic characteristics, namely, California’s Central Valley (together with the Sierra Nevada) and the European Alps. NCEP/NCAR reanalysis data are used to represent the large scale state of the atmosphere over the regions. The assessment is based on simulating daily precipitation for 103 stations for the month of January, for the years 1950–2004 in the California region, and for 70 stations in the European Alps (January 1948–2004). Generally, simulated precipitation is in better agreement with observations in the California region than in the European Alps. Similarity measures such as the Euclidean norm, the sum of absolute differences and the angle between two atmospheric states perform better than measures which introduce additional weightings to principal components (e.g., the Mahalanobis distance). The best choice seems dependent upon the target variable. Lengths of wet spells, for instance, are best simulated by using the angular similarity measure. Overall, the Euclidean norm performs satisfactorily in most cases and hence is a reasonable first choice, whereas the use of Mahalanobis distance is less advisable. The performance of the analog method improves by including large-scale information for bygone days, particularly, for the simulation of wet and dry spells. Optimal performance is obtained when about 85–90% of the total predictor variability is retained.

The output of several multi-century simulations with a coupled ocean–atmosphere general circulation model is examined with respect to the variability of global storm activity in winter on time scales of decades and longer. The frequency of maximum wind speed events within a grid box, using the lower limits on the Beaufort wind speed scale of 8 and 10 Bft as thresholds, is taken as the characteristic parameter. Two historical climate runs with time-dependent forcing of the last five centuries, one control simulation, and three climate change experiments are considered. The storm frequency shows no trend until recently. Global maps for the industrially influenced period hardly differ from pre-industrial maps, even though significant temperature anomalies temporarily emerge in the historical runs. Two indicators describing the frequency and the regional shift of storm activity are determined. In historical times they are decoupled from temperature. Variations in solar and volcanic forcing in the historical simulations as well as in greenhouse gas concentrations for the industrially influenced period are not related to variations in storm activity. Also, anomalous temperature regimes like the Late Maunder Minimum are not associated with systematic storm conditions. In the climate change experiments, a poleward shift of storm activity is found in all three storm track regions. Over the North Atlantic and Southern Ocean, storm activity increases, while it decreases over the Pacific Ocean. In contrast to the historical runs, and with the exception of the North Pacific storm frequency index, the storm indices parallel the development of temperature, exceeding the 2 σ-range of pre-industrial variations in the early twenty-first century.

Large-scale climate forcings on local precipitation in Cameroon are analysed during the little rainy season (March-June). Variables found to have strong influence are used to downscale GCM projected rainfall for 2010-2049. In particular, 2 IPCC IS92a scenarios, simulated by the ECHAM4/OPYC3 climate model, are investigated. First, monthly precipitation data from 1951-1990 at 33 meteorological stations are grouped into homogeneous rainfall regions using self-organising feature maps (SOFMs). SOFMs identified 3 groups of stations with related time-series variability. Then, an empirical orthogonal function procedure, followed by canonical correlation analysis (CCA), is used to derive statistical relationships between the homogeneous regions and large-scale variables from the NCEP/NCAR Reanalysis Project. A CCA model is established for every region. Numerous fields at different pressure levels are used as macro-scale predictors. All possible combinations of 2 predictors are systematically tested in 3 validation experiments. Those combinations that perform well in the experiments are used to derive local-scale precipitation scenarios from the general circu- lation model (GCM) climate projection experiments. Different combinations of large-scale variables enter the model depending on region. A composite analysis suggests that precipitation is related to an advective (convective) phenomenon in the northern (southern) part of the study domain. More- over, precipitation changes based on 2 IS92a emission scenarios as simulated by ECHAM4/OPYC3 are calculated. The trace-gas-only and the trace-gas-plus-sulphate integrations induce changes ranging locally from + 44 to -10% and from + 36 to -9% respectively, relative to the 1951-1990 control period.

In the central North Sea we have observed an increase in the frequency of eastwardly propagating waves in the last 4 decades. To assess the significance of this change, wave statistics for the 20th century were reconstructed with a statistical model. With a linear multivariate technique (redundancy analysis), monthly mean air pressure fields over the North Atlantic and Western Europe were downscaled on the intramonthly frequency of directional wave propagation. When compared against this reference, the recent change appears statistically significant at the 5% level. In order to investigate the reason for this local climatic change, the reconstruction was compared with the down- scaled results of control and transient GCM scenarios (ECHAM4-OPYC3) and with the results obtained in a high-resolution time-slice experiment with increased concentrations of greenhouse gases and aerosols. Both estimates are qualitatively consistent with the changes observed in the last 4 decades. We suggest that the recent increase in eastward propagation is a local manifestation of anthropogenic global climate change.

The influence of a changing wind climate on the sea surface elevations along the North Sea coast was investigated, with a statistical down-scaling technique and with a dynamical model. Firstly, in an analysis of past variability the two models were run for different periods: the numerical model for the winters 1955–1993 and the statistical one for the winters 1899–93. Secondly, a fine-scale time slice experiment for a control run and a scenario for doubled atmospheric carbon dioxide concentration was used (in both the dynamical and the statistical down-scaling model) to assess the sea level related changes due to an (anthropogenic) increase in atmospheric carbon dioxide concentration. Both models agree on the following results: (a) In the past, the winter means of high water levels along the North Sea coast increase on the order of 1–2 mm/yr, on account of only the atmospheric forcing; (b) the high intramonthly percentiles – reduced by the winter averages – show no clear trend; and (c) the British Coast exhibits a slight negative and the continental coast an equally small positive tendency. In the climate scenarios, the effect on the high percentiles follows the same pattern. A slightly larger and everywhere positive difference is diagnosed in the mean water levels along the North Sea coast. Together with the hindcast result, the above interpretations might suggest a continuing increase of mean water levels at the North Sea coast due to an increasing CO2 concentration in the atmosphere; this would occur throughout the second half of this century and, possibly, beyond. The high frequency variability appears to be much less affected. However, the natural variability of the system is too strong to clearly identify such a process, or attribute it to anthropogenic development. This study evaluates the impact of a changing atmospheric forcing only. Additional influences, such as the eustatic and isostatic effects, are not taken into account.

Improved observations in the tropical Pacific during the Tropical Ocean-Global Atmosphere (TOGA) program have served to corroborate preexisting notions concerning the seasonally dependent relationships between sea surface temperature, sea level pressure, wind stress, rainfall, upper tropospheric circulation, and ocean thermal structure anomalies in the El Nino-Southern Oscillation (ENSO) phenomenon. However, the paradigm of a quasiperiodic “ENSO cycle,” phase locked with the annual march, does not capture the complexity of the evolution of the anomalies. The inadequacy of this model was particularly apparent during the second half of TOGA when the variability was highly aperiodic. Also, a single modal structure or empirical orthogonal function does not appear to be capable of representing the range of spatial patterns of ocean-atmosphere interaction in the tropical Pacific. These results suggest the need for a more inclusive phenomenological description of ENSO. Data collected during TOGA serve to confirm the influence of tropical Atlantic sea surface temperature anomalies upon rainfall in northeast Brazil.

Different time scales of climate change and their differential perceptionin society are discussed. A historical examination of natural climate changes duringthe past millennium suggests that short-term changes, especially crucial changes,trigger a significant response in and by society. Short-term changes correspond to the"time horizon of everyday life", that is, to a time scale from days and weeks to a fewyears. The anticipated anthropogenic climate changes, however, are expected to occuron a longer time scale. They require a response by society not on the basis of primaryexperience but on the basis of scientifically constructed scenarios and ways in whichsuch information is represented in the modern media for example. Socio—economicimpact research relies on concepts that are based on the premise of perfectly informedactors for the development of optimal adaptation strategies. In contrast to such aconception, we develop the concept of a "social construct of climate" as decisive forthe public perception of scientific knowledge about climate and for public policy onclimate change. The concept is illustrated using a number of examples.

[1] We investigate whether the observed surface specific humidity (q) trends over the Mediterranean region in the period 1974–2003 are consistent with climate model (CMIP3, CMIP5) simulations of q in response to anthropogenic forcing (Greenhouse gas and sulphate aerosols). The natural (internal) variability is estimated using 6,000-year of pre-industrial control simulations. With the exception of winter, the increases in annual and seasonal q over this region are very unlikely (with less than 1%chance) due to natural (internal) variability or natural forcing alone. Using several climate models and ensemble means, we demonstrate that the large-scale component (spatial-mean trend) of the anthropogenic forcing is detectable (at 1% level) in the annual and seasonal trends of q (except winter). However, the smaller-scale component (spatial anomalies about the mean trend) of the anthropogenic signal is detectable only in warm seasons (spring and summer). We further show that the spread of projected trends based on the A1B scenario derived from 13 CMIP3 models encompasses the observed area-averaged trend in q. This may imply that the observed trends of surface humidity, which is an important factor in human thermal comfort, serves as an illustration of plausible future expected change in the region.

This study investigates storminess in northern Italy and the northern Adriatic Sea through the examination of several storm proxies. These proxies are based on homogenized daily mean pressure series given at a set of stations (Genoa, Milan, Padua, Turin, and Hvar). The application of widely accepted and well-known methods on pressure series allows for a long-term year-to-year analysis of the intra-seasonal storm variability. As storminess is usually more intense throughout the cold season, our analysis is limited to the October–March period of each year. The following proxies are considered in this study: First, we assess the statistics of geostrophic wind speed. These statistics are derived from two adjacent triangles that are located across the Adriatic Sea (Padua–Hvar–Genoa) and in northern Italy (Genoa–Padua–Turin). Second, we evaluate annual statistics of time series of pressure tendency. Last, intra-seasonal low percentiles of pressure are also made use of. These proxies are used to describe the evolution of the storm climate far back in time, covering in some cases a 260-year long period. The proxies show pronounced interannual and interdecadal variability, but no sustained long-term trend.

A hierarchy of ENSO (El Nino/Southern Oscillation) prediction schemes has been developed which includes statistical schemes and physical models. The statistical models are, in general, based on advanced statistical techniques and can be classified into models which use either low-frequency variations in the atmosphere (sea level pressure or surface wind) or upper ocean heat content as predictors. The physical models consist of coupled ocean-atmosphere models of varying degrees of complexity, ranging from simplified coupled models of the lsquoshallow waterrsquo-type to coupled general circulation models. All models, statistical and physical, perform considerably better than the persistence forecast on predicting typical indices of ENSO on lead times of 6 to 12 months. The most successful prediction schemes, the fully physical coupled ocean-atmosphere models, show significant prediction abilities at lead times exceeding one year period. We therefore conclude that ENSO is predictable at least one year in advance. However, all of this applies to gross indices of ENSO such as the Southern Oscillation Index. Despite the demonstrated predictability, little is known about the predictability of specific features known to be associated with ENSO (e.g. Indian Monsoon rainfall, Southern African drought, or even off-equatorial sea surface temperature). Nor has the relative importance for prediction of different regional anomalies or different physical processes yet been established. A seasonal dependence in predictability is well established, but the processes responsible for it are not fully understood.

Sea level rise, especially combined with possible changes in storm surges and increased river discharge resulting from climate change, poses a major threat in low-lying river deltas. In this study we focus on a specific example of such a delta: the Netherlands. To evaluate whether the country's flood protection strategy is capable of coping with future climate conditions, an assessment of low-probability/high-impact scenarios is conducted, focusing mainly on sea level rise. We develop a plausible high-end scenario of 0.55 to 1.15 m global mean sea level rise, and 0.40 to 1.05 m rise on the coast of the Netherlands by 2100 (excluding land subsidence), and more than three times these local values by 2200. Together with projections for changes in storm surge height and peak river discharge, these scenarios depict a complex, enhanced flood risk for the Dutch delta.

[1] Previous statistical detection methods based partially on climate model simulations indicate that, globally, the observed warming lies very probably outside the natural variations. We use a more simple approach to assess recent warming at different spatial scales without making explicit use of climate simulations. It considers the likelihood that the observed recent clustering of warm record-breaking mean temperatures at global, regional and local scales may occur by chance in a stationary climate. Under two statistical null-hypotheses, autoregressive and long-memory, this probability turns to be very low: for the global records lower than p = 0.001, and even lower for some regional records. The picture for the individual long station records is not as clear, as the number of recent record years is not as large as for the spatially averaged temperatures.

In recent research, my associates and I presented a complex set of mathematical scenarios in which developed market economies, characterized by mature and new economy industries, evolve into two (or more) geographic regions. The first, almost invariably coastal or along the banks of rivers, is more attractive to investment in both mature and new economy industries, but is subject to higher risk, both market and environmental. Growth in this first sector is innovation and knowledge intensive, characterized by clustering of similar labor skills and firms, and the instigator of extensive net migration. The second sector is less dynamic and often less developed, but subject to less environmental risk. Climatic, natural, and environmental hazards are undergoing change and may, in some cases, become more threatening. Economies, however, are growing, enabling them to adapt to or even mitigate the damaging consequences of these natural hazards. In this paper, I present an economic growth model set in the two sectors and facing damaging hazards. The hazards are of varying predictability, ranging between quantitative risk and subjective uncertainty.

Received 16 November 2005; revised 12 December 2005; accepted 15 December 2005; published 1 February 2006. [1] By means of simulations with a global coupled AOGCM it is shown that changes in the polar energy sink region can exert a strong influence on the mid- and high-latitude climate by modulating the strength of the mid-latitude westerlies and storm tracks. It is found, that a more realistic sea-ice and snow albedo treatment changes the ice-albedo feedback and the radiative exchange between the atmosphere and the ocean-sea-ice system. The planetary wave energy fluxes in the middle troposphere of mid-latitudes between 30 and 50 Na re redistributed, which induces perturbations in the zonal and meridional planetary wave trains from the tropics over the mid-latitudes into the Arctic. It is shown, that the improved parameterization of Arctic sea-ice and snow albedo can trigger changes in the Arctic and North Atlantic Oscillation pattern with strong implications for the European climate. Citation: Dethloff, K., et al. (2006), A dynamical link between the Arctic and the global climate system, Geophys. Res. Lett., 33, L03703, doi:10.1029/2005GL025245.

A heat-conduction forward model driven by ground surface temperature from three 1000-year climate simulations with the state-of-the-art ECHO-g model has been used to simulate underground temperature perturbation profiles. An inversion approach has been applied to reconstruct ground surface temperature histories from the simulated profiles and to compare them with the climate model temperatures. Results support the skill of borehole inversion methods to retrieve long-term temperature trends, and the robustness of using the present-day borehole network for reconstructing SAT variations.

The spatial patterns of surface air-temperature variations in the period 1000 to 2100, simulated with the ECHO-G atmosphere-ocean coupled model, are analyzed. The model was driven by solar, volcanic and greenhouse gas forcing. The leading mode of temperature variability in the preindustrial period represents an almost global coherent variation of temperatures, with larger amplitudes over the continents and Northern Hemisphere. This mode also describes a large part of the spatial structure of the warming simulated in the 21st century. However, in the 21st century, regional departures from this spatial structure are also present and can be ascribed to atmospheric circulation responses to anthropogenic forcing in the last decades of the 21st century.

[1] The relationship between terrestrial deep soil temperature (TDST) and surface temperature (SAT) at interannual and centennial timescales has been investigated in a simulation of the last millennium with a three-dimensional climate model driven by estimations of historical external forcing. TDST is loosely related to borehole temperature profiles, which have been recently used to reconstruct long term temperature trends in the last centuries. Recently, questions about the validity of boreholes-based reconstructions have been raised. In the simulation, at interannual time scales the connection between TDST and SAT is stable, being stronger in the summer half year than in the winter half year. At long timescales, annual TDSL is a good proxy for annual SAT, and their variations are almost indistinguishable from each other. Both TDSL and terrestrial SAT overestimate the variations of global mean SAT. This may be a source for the disagreement between statistical reconstructions of global SAT and terrestrial borehole measurements.

Data assimilation (DA) can be defined as the incorporation of measurements into the numerical model of a physical system, to improve the forecasts of this model. Data assimilation has gained increasing popularity in the atmospheric and oceanographie communities over the last two decades. The random functions approach of geostatistics, its multivariate spatial modeling tools, its change-of-support models and the spatial simulation capabilities provide an attractive framework for performing data assimilation (Bertino et al., 2000).

Regional atmospheric models have matured in the past few years. They now find broad applications that range from process studies, reconstruction of recent and paleoclimates, simulation of pathways and deposition of anthropogenic matter, construction of plausible scenarios of future climate change, and regional weather analysis. The initial major problem—namely, the ill-posed boundary value problem—has been efficiently fixed by including a sponge zone that ensures the consistency of the interior flow with the boundary conditions at the outflow boundaries, or by introducing large-scale control in the interior. With these setups, regional models may now be integrated stably over many decades and thereby provide information on spatial and temporal scales not sufficiently resolved by present-day global climate models and weather analyses.

In the past decade, climate research has made considerable progress in understanding and modeling climate on timescales of years to decades. At the same time, increasingly more proxies of climatic variables have been detected. Significant progress has been achieved in aligning this evidence in time. A workshop earlier this year on the state of the art of climate variability studies called for utilizing coupled atmosphere-ocean general circulation models (GCMs) for simulating and analyzing paleoclimatic variability. A fresh attempt is warranted to systematically combine the skills of climate modelers, climate diagnosticians, and paleoclimatologists, agreed participants at the workshop, “Climate Variability on Multidecadal to Millennial Timescales,” sponsored jointly by the Royal Netherlands Meteorological Institute KNMI and the German Federal Research Laboratory GKSS.

General circulation models of the atmosphere have been used to investigate the climate response to factors such as the changing concentration of CO 2 . Their usefulness is restricted by the need to specify the sea surface temperature. Partial solutions to this problem exist, such as adding a model of the ocean mixed layer to the atmosphere model, but these cannot simulate the response of the ocean heat transport to changes in the atmospheric circulation. Only a coupled atmosphere—ocean-sea-ice model can represent the mechanisms that determine the climate on time scales of decades. A coupled atmosphere-ocean-sea-ice model has been developed at the Meteorological Office. This paper describes the ocean and sea-ice components of that model and some of the characteristics of the ocean model when driven by observed fluxes of heat, fresh water, and momentum during a long spin-up experiment. Aspects of a four-year integration of the coupled model are discussed. Many factors contribute to the simulation of the coupled model. Not only are the characteristics of the component models present, but the additional degrees of freedom introduced by the removal of fixed boundary conditions at the ocean surface also introduce new features into the simulation. Particular features that result from the interaction of the models used in the simulations described in this paper include a feedback between the sea-ice model and the simulations of the atmosphere model at high latitudes, and a warming of the tropical Pacific.

A coupled ocean-atmosphere general circulation model has been developed for TOGA related problems. The coupled model consists of an ocean model of the tropical Pacific and a global low-order spectral atmosphere model. The two models interact via wind stress and sea surface temperature. In order to avoid a climate drift within the coupled model, a flux correction method is applied.Experiments were performed by introducing a westerly wind stress burst over the western equatorial Pacific for one month. Thereafter, the wind burst is turned off and the response of the coupled model to the initial disturbance is investigated. The results are compared with the response of the ocean model run with the same disturbance in an uncoupled mode.It is shown that the coupling leads to a significant increase of the duration of anomalous conditions in the ocean. SST anomalies persist for about 12 months in the coupled run, while they have already disappeared after 4 months in the uncoupled case. The increase in persistence is due to the feedback of the atmosphere, which responds with an eastward shift of the ascending branch of the Walker Circulation.In a second experiment with the coupled model the initial disturbance was introduced within another season. The results show no basic differences to the results of the first experiment.An interesting result of the coupled model runs is the occurrence of spontaneous westerly wind bursts over the western Pacific, which developed by internal dynamics. Location and duration of these spontaneous wind bursts show some correspondence with the time-space structure of observed westerly wind stress episodes over the western Pacific.

An atmospheric general circulation model (GCM) was forced with the observed near-global sea surface temperature (SST) pattern for the period January 1970-December 1985. Its response over the Pacific Ocean is compared with Tahiti and Darwin station sea-level pressure and wind stress analyses obtained from Florida State University. The time-dependent SST clearly induces in the model run a Southern Oscillation that is apparent in the time series of all considered variables. The phase of the GCM Southern Oscillation is as observed but its low-frequency variance is too low and the spatial pattern is confined mainly to the western Pacific. The model is successful in reproducing the warm events of 1972—73 and 1982—83 and the cold event 1970—71, but fails with the cold events 1973-74 and 1975-76 and with the warm event 1976-77. Because the GCM is used as the atmospheric component in a coupled model, the response of an equatorial oceanic primitive equation model to both the modelled and observed wind stress is examined. The ocean model responds in essentially the same way to forcing with the observed wind stress and to forcing that corresponds to the first two low-frequency empirical orthogonal functions (EOFS) of the wind variations. These first two EOFS describe a regular eastward propagation of the so signal from the western Pacific to the central Pacific within about one year. The ocean model’s response to the modelled wind stress is too weak. It is similar to the response to the first observed wind stress EOF only. That is, the observed Southern Oscillation appears as a sequence of propagating patterns but the simulated Southern Oscillation appears as one standing pattern. The nature of the deviation of simulated wind stress from observations is further analysed by means of model output statistics.

Based on the statistical approach proposed by von Storch et al. (1993 i J. Clim. 6: 1161-1171), a general, flexible method to assess changes in the local climatic inputs of ecosystem models from largescale climatic changes as simulated by general circulation models (GCMs) was developed, verified and applied at 5 Swiss locations for the summer and winter seasons. According to the requirements of various ecosystem models, at each location 17 seasonal statistics related to daily temperatures, precipitation, sunshine duration, air humidity and wind speed were considered. Year-to-year variations of the local variables were linked by means of Canonical Correlation Analysis to simultaneous anomalies in the North Atlantic/European sea-level pressure and near-surface temperature'fields. The analysis was performed for the period 1901 to 1940, separately for each season and location. In all cases, physically plausible statistical models were found which quantified the local effects of changes in major circulation patterns, such as the strength of westerly flow in winter and of large-scale subsidence in summer. In the verification interval 1941 to 1980, most variables were better reconstructed in winter than in summer, and better at the 3 north alpine than at the 2 south alpine locations. The best-reconstructed variables were seasonal mean daily temperatures and daily temperature extremes, for which on average 42 to 75 % of the total variances over the verification interval at the different locations could be explained. Explained variances for precipitation totals were 29 to 55% in winter and 10 to 28% in summer, and for mean daily relative sunshine durations in both seasons ca 12 to 530/0. Seasonal mean relative humidities, mean wind speeds, and within-summer standard deviations of daily variables were generally poorly reproduced. It was found that the procedure of von Storch et al. can be generally improved by using, in addition to sea-level pressure, the near-surface temperature as a large-scale predictor. Improvement was strongest for temperaturerelated variables, and for the summer season. The established statistical relationships were applied to anomaly fields as simulated by the Hamburg fully coupled atmospheric/oceanic ECHAM1/LSG GCM under increasing atmospheric greenhouse gas concentrations. The procedure yields time-dependent, internally consistent, and regionally strongly differentiated climatic change estimates for several important ecosystem inputs, at a spatial resolution far above the resolution of present GCMs.