Using simulations performed with 24 coupled atmosphere–ocean global climate models from phase 5 of the Coupled Model Intercomparison Project (CMIP5), projections of Northern Hemisphere daily snowfall events under the RCP8.5 emissions scenario are analyzed for the periods of 2021–50 and 2071–2100 and compared to the historical period of 1971–2000. The overall frequency of daily snowfall events is simulated to decrease across much of the Northern Hemisphere, except at the highest latitudes such as northern Canada, northern Siberia, and Greenland. Seasonal redistributions of daily snowfall event frequency and average daily snowfall are also projected to occur in some regions. For example, large portions of the Northern Hemisphere, including much of Canada, Tibet, northern Scandinavia, northern Siberia, and Greenland, are projected to experience increases in average daily snowfall and event frequency in midwinter. But in warmer months, the regions with increased snowfall become fewer in number and are limited to northern Canada, northern Siberia, and Greenland. These simulations also show changes in the frequency distribution of daily snowfall event intensity, including an increase in heavier snowfall events even in some regions where the overall snowfall decreases. The projected changes in daily snowfall event frequency exhibit some dependence on the temperature biases of the individual models in certain regions and times of the year, with colder models typically toward the positive end of the distribution of event frequency changes and warmer models toward the negative end, particularly in regions near the transition zone between increasing and decreasing snowfall.

In a multi-agent transportation simulation, each traveler is represented individually. Such a simulation consists of at least the following modules: (i) Activity generation. For each traveler in the simulation, a complete 24-hour day-plan is generated, with each major activity (sleep, eat, work, shop, drink beer), their times, and their locations. (ii) Modal and route choice. For each traveler in the simulation, the mode of transportation and the actual routes are computed. (iii) The Traffic simulation itself. In this module, the travelers are moved through the system, via the transportation modes they have chosen. (iv) Learning and feedback. In order to find solutions which are consistent between the modules, a relaxation technique is used. This technique has similarities to day-today human learning and can also be interpreted that way.-Besides, one needs input data, such data of the road network, or (synthetic) populations. In the future, further modules need to be added, such as for housing and land use, or for freight traffic. Using advanced computational methods, in particular parallel computing, it is now possible to do this for large metropolitan areas with 10 million inhabitants or more. We are currently working on such a simulation of all of Switzerland. Our focus is on a computationally efficient implementation of the agent-based representation, which means that we in fact represent each agent with an individual set of plans as explained above. We use a database to store the agent's strategies, then load them into the simulation modules as required, and feed back individual performance measures into the database. This approach allows that additional modules can be coupled easily, and without destroying computational performance. 1 Assuming 3 to 3.5 trips per person per day, this will result in about 20-25 million trips. This number includes pedestrian trips (like walking to lunch), trips by public transit, freight traffic, etc. The number of car trips on a typical weekday in Switzerland is currently about 5 million (see Vrtic (2001) for where the data comes from). The goal of our study is twofold: © Investigate the computational challenges and how they can be overcome. © Investigate what is necessary to make a simulation system realistic enough to be useful for such a scenario, and how difficult this is. This paper gives a report on the current status. Section 2 describes the simulation modules and how they were used for the purposes of this study. Section 3 describes the input data, i.e. the underlying network and the demand generation. Besides "normal" demand, we also describe one where 50 000 travelers travel from random starting points within Switzerland to the Ticino, which is the southern part of Switzerland. We use this second scenario as a plausibility test for routing and feedback. This is followed by Sect. 4, which describes some results and Sect. 5, which describes issues related to computational performance of the parallel micro-simulation. The paper ends with a discussion and a summary. 2 S...

In many transportation simulation applications including intelligent transportation systems (ITS), behavioral responses of individual travelers are important. This implies that simulating individual travelers directly may be useful. Such a microscopic simulation, consisting of many intelligent particles (= agents), is an example of a multi-agent simulation. For ITS applications, it would be

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