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Article

Downscaling Climate Information  

Rasmus Benestad

What are the local consequences of a global climate change? This question is important for proper handling of risks associated with weather and climate. It also tacitly assumes that there is a systematic link between conditions taking place on a global scale and local effects. It is the utilization of the dependency of local climate on the global picture that is the backbone of downscaling; however, it is perhaps easiest to explain the concept of downscaling in climate research if we start asking why it is necessary. Global climate models are our best tools for computing future temperature, wind, and precipitation (or other climatological variables), but their limitations do not let them calculate local details for these quantities. It is simply not adequate to interpolate from model results. However, the models are able to predict large-scale features, such as circulation patterns, El Niño Southern Oscillation (ENSO), and the global mean temperature. The local temperature and precipitation are nevertheless related to conditions taking place over a larger surrounding region as well as local geographical features (also true, in general, for variables connected to weather/climate). This, of course, also applies to other weather elements. Downscaling makes use of systematic dependencies between local conditions and large-scale ambient phenomena in addition to including information about the effect of the local geography on the local climate. The application of downscaling can involve several different approaches. This article will discuss various downscaling strategies and methods and will elaborate on their rationale, assumptions, strengths, and weaknesses. One important issue is the presence of spontaneous natural year-to-year variations that are not necessarily directly related to the global state, but are internally generated and superimposed on the long-term climate change. These variations typically involve phenomena such as ENSO, the North Atlantic Oscillation (NAO), and the Southeast Asian monsoon, which are nonlinear and non-deterministic. We cannot predict the exact evolution of non-deterministic natural variations beyond a short time horizon. It is possible nevertheless to estimate probabilities for their future state based, for instance, on projections with models run many times with slightly different set-up, and thereby to get some information about the likelihood of future outcomes. When it comes to downscaling and predicting regional and local climate, it is important to use many global climate model predictions. Another important point is to apply proper validation to make sure the models give skillful predictions. For some downscaling approaches such as regional climate models, there usually is a need for bias adjustment due to model imperfections. This means the downscaling doesn’t get the right answer for the right reason. Some of the explanations for the presence of biases in the results may be different parameterization schemes in the driving global and the nested regional models. A final underlying question is: What can we learn from downscaling? The context for the analysis is important, as downscaling is often used to find answers to some (implicit) question and can be a means of extracting most of the relevant information concerning the local climate. It is also important to include discussions about uncertainty, model skill or shortcomings, model validation, and skill scores.

Article

Historical Documents as Proxy Data in Venice and Its Marine Environment  

Dario Camuffo

The environmental history of Venice over the last millennium has been reconstructed from written, pictorial, and architectural documentary sources, used in a synergistic way. The method of transforming a document into an index and then into calibrated numerical values according to an international system of units has been applied in the case of Venice and its geographical and climate peculiarities. Because frost constituted a dramatic challenge for the city, a series of severe winters is well documented: The city was sieged by ice, meaning Venetians had to cross the ice transporting food, beverages, and wood for burning in carts, as recorded in written reports and visual representations. The sea level in the 18th century has been reconstructed based on paintings by Canaletto and Bellotto, who took advantage of a camera obscura to precisely draw the views of the city and its canals.. These paintings accurately represent the green algae belt that corresponds to the level of soaking created by marine waters at high tide. This has made it possible to measure how much the green algae (and therefore the seawater) has risen since the 18th century. Similarly, a painting by Veronese has enabled the reconstruction of sea level rise (SLR) since 1571. Another useful proxy is the water stairs of the Venetian palaces. These were originally built to access boats and are now (almost) totally submerged and covered with algae. As the sea level rose, these steps became submerged underwater. The depth of the lowest step is therefore representative of how much the sea level rose after the stair was built. This proxy has allowed the relative sea level since 1350 to be reconstructed, and an exponential trend in the rising of the sea level has been identified. Venice has at times been flooded by seawater, including tsunamis at the beginning of the second millennium. A long series of sea floods due to storm surges triggered by particular meteorological situations shows that the flooding frequency is related to the exponential SLR. In the 1960s, there was a sharp increase in frequency of flooding, which coincided with the digging of deep and wide canals, excavated to allow the passage of tankers. This increased the exchange of water between the sea and the lagoon. Proxies based on archaeological remains, as well as geological-biological cores extracted from the coastal area and dated with isotopic methods, cover long time periods; the longest record reaching 13 ka BP. However, the time resolution is reduced, thus providing good data for physical geography purposes.