A quant itative method for measuring the wet tahility of porous media containing brine and crude oil has been developed by the USBM using C@il/CZ?yp TeSSUIE?

No abstract

In the EU-Project ENSEMBLES, a major objective was the development of a weighting system for regional climate models (RCMs) based on the ability of the RCMs to reproduce observed characteristics of important atmospheric variables. Within this context, a suite of state-of-the-art RCMs was employed for the creation of probabilistic future regional climate change scenarios for Europe. Among others, one measure to determine the individual RCM-weights is their ability to reproduce the observed trends in 2 m temperature in the reanalysis (ERA40)-driven experiments for the period 1960-2000. As the reference, we used the new ENSEMBLES observational gridded dataset for Europe (E-OBS). As additional datasets for comparisons, we also used the near-surface temperature datasets from the CRU observations and from the ERA40 and NCEP/NCAR reanalysis. The analysis was performed for the land fraction of 8 different European regions, the so-called PRU-DENCE regions defined within the PRUDENCE project (http://prudence.dmi.dk). Annual and seasonal linear trends in near-surface temperature were computed for each ENSEMBLES RCM, the E-OBS dataset, and for the additional datasets mentioned above. In all regions, the computed linear temperature trends based on annual mean temperatures showed smaller values for the RCMs and the NCEP/NCAR reanalysis than for both observational datasets, and in most regions also smaller values than for the ERA40 reanalysis dataset. Depending on the magnitude of the difference in linear trends between the individual RCMs and the E-OBS dataset, skill scores were assigned to each RCM. The resulting skill scores were of similar magnitude (0.7-0.9) for the different models and regions (except for Scandinavia, which had lower skill scores around 0.6-0.8). Spatially aggregated for all of Europe, and combined from annual and seasonal into one value for each RCM, these skill scores were included in the general ENSEMBLES RCM weighting system

Precipitation data from long-term high-resolution simulations with two regional climate models (CLM and REMO) are evaluated using a climatology based on observations for south-western Germany. Both models are driven by a present day climate forcing scenario from the global climate model ECHAM5. The climatological evaluation shows a strong seasonal dependence of the model deficiencies. In spring and summer there are relatively small differences between simulation results and observations. But during winter both the regional models and ECHAM5 strongly overestimate the precipitation. The frequency distributions of the model results agree well with observed data. An overestimation of the precipitation at the upwind sides of mountainous areas occurs in the regional simulations. We found that the coupling of the regional models to the driving model is stronger in winter than in summer. Therefore, in winter the large scale model have a larger impact on the performance of the regional simulations. During summer the benefit of regional climate simulations is higher. ZusammenfassungDie vorliegende Arbeit beschreibt die Ergebnisse der Evaluierung des Niederschlags von zwei hoch aufgelösten Simulationen mit den regionalen Klimamodellen CLM und REMO für die Region Südwest-Deutschland. Dazu wurde eine auf Beobachtungen basierende Klimatologie benutzt. Die Randwerte beider regionalen Modelle stammen von dem globalen Klimamodell ECHAM5, welches Antriebsdaten für das zwanzigste Jahrhundert verwendet. Die klimatologische Evaluierung zeigt eine starke jahreszeitliche Abhängigkeit derÜbereinstimmung. Beide regionalen Modelle zeigen geringe Abweichungen zu den Beobachtungen im Frühjahr und Sommer. Im Winter allerdingsüberschätzen sowohl CLM und REMO als auch ECHAM5 die Niederschläge deutlich. Die Häufigkeitsverteilung des Niederschlags stimmt bei den Modellergebnissen gut mit den Beobachtungenüberein. In den regionalen Simulationen tritt einë Uberschätzung der Niederschläge an den windwärts gelegenen Seiten von gebirgigen Regionen auf. Die vorliegende Studie zeigt, dass die Kopplung zwischen regionalem und globalem Modell im Winter stärker ist als im Sommer. Daher hat das Verhalten des globalen Modells im Winter einen größeren Einfluss. Im Sommer ist der Gewinn durch die regionalen Klimasimulationen höher.

No abstract

Over the first half of 2020, Siberia experienced the warmest period from January to June since records began and on the 20th of June the weather station at Verkhoyansk reported 38 °C, the highest daily maximum temperature recorded north of the Arctic Circle. We present a multi-model, multi-method analysis on how anthropogenic climate change affected the probability of these events occurring using both observational datasets and a large collection of climate models, including state-of-the-art higher-resolution simulations designed for attribution and many from the latest generation of coupled ocean-atmosphere models, CMIP6. Conscious that the impacts of heatwaves can span large differences in spatial and temporal scales, we focus on two measures of the extreme Siberian heat of 2020: January to June mean temperatures over a large Siberian region and maximum daily temperatures in the vicinity of the town of Verkhoyansk. We show that human-induced climate change has dramatically increased the probability of occurrence and magnitude of extremes in both of these (with lower confidence for the probability for Verkhoyansk) and that without human influence the temperatures widely experienced in Siberia in the first half of 2020 would have been practically impossible.

Abstract. The properties of European windstorms under present climate conditions are estimated on the basis of surface wind forecasts from the European Centre for MediumRange Weather Forecast (ECMWF) Ensemble Prediction System (EPS). While the EPS is designed to provide forecast information of the range of possible weather developments starting from the observed state of weather, we use its archive in a climatological context. It provides a large number of modifications of observed storm events and includes storms that did not occur in reality. Thus it is possible to create a large sample of storm events, which entirely originate from a physically consistent model, whose ensemble spread represents feasible alternative storm realizations of the covered period. This paper shows that the huge amount of identifiable events in the EPS is applicable to reduce uncertainties in a wide range of fields of research focusing on winter storms. Windstorms are identified and tracked in this study over their lifetime using an algorithm based on the local exceedance of the 98th percentile of instantaneous 10 m wind speed, which is associated with a storm severity measure. After removing inhomogeneities in the data set arising from major modifications of the operational system, the distributions of storm severity, storm size, and storm duration are computed. The overall principal properties of the homogenized EPS storm data set are in good agreement with storms from the ERAInterim data set, making it suitable for climatological investigations of these extreme events. A demonstrated benefit in the climatological context by the EPS is presented. It gives clear evidence of a linear increase of maximum storm intensity and wind field size with storm duration. This relation is not recognizable from a sparse ERA-Interim sample for long-lasting events, as the number of events in the reanalysis is not sufficient to represent these characteristics.

[1] The influence of regional scale information on the global circulation has been investigated through the development and application of the so-called ''two-way nesting'' technique. The two-way nesting climate model system consists of a spectral atmospheric general circulation model and a grid point regional atmospheric model. Two simulations of 10 years each have been carried out with and without the feedback of the regional model to the global model. For the regional model domain the Maritime Continent covering the tropical area of the Indonesian islands has been selected as a key region influencing the global climate. The analysis of the global atmospheric temperature field shows a clear improvement through the regional model feedback; the systematic error with respect to re-analysis data can be reduced even in regions far away from the regional model domain. Citation: Lorenz, P., and D. Jacob (2005), Influence of regional scale information on the global circulation: A two-way nesting climate simulation,