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Abstract. The Interior of Western Canada, up to and including the Arctic, has experienced rapid change in its climate, hydrology, cryosphere and ecosystems and this is expected to continue. Although there is general consensus that warming will occur in the future, many critical issues remain. In this first of two articles, attention is placed on atmospheric-related issues that range from large scales down to individual precipitation events. Each of these is considered in terms of expected change organized by season and utilizing climate scenario information as well as thermodynamically-driven future climatic forcing simulations. Large scale atmospheric circulations affecting this region are generally projected to become stronger in each season and, coupled with warming temperatures, lead to enhancements of numerous water-related and temperature-related extremes. These include winter snowstorms, freezing rain, drought as well as atmospheric forcing of spring floods although not necessarily summer convection. Collective insights of these atmospheric findings are summarized in a consistent, connected physical framework.
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Abstract. To put in perspective the recent climate change, it is necessary to extend the instrumental climate records with proxy data from palaeoclimate archives. Arctic climate variability for the last two millennia has been investigated using statistical and signal analyses from three regionally averaged records from the North Atlantic, Siberia and Alaska based on many sort of proxy data archived in the Arctic 2k database. In the North Atlantic and Alaska areas, the major climatic trend is characterized by long-term cooling interrupted by the recent warming that started at the beginning of the 19th century. This cooling trend is not clearly visible in the Siberian region. The Little Ice Age (LIA) was identified from the individual series and is characterized by an important spatial and temporal expression of climate variability. It started at the earliest by around 1200 AD and ended at the latest in the middle of the 20th century. The large spread temporal coverage of LIA did not show regional consistency or particular spatial distribution and did not show relationship with archive/proxy type either. A focus on the last two centuries shows a recent warming characterized by a well-marked warming trend paralleling with increasing greenhouse gas emissions. It also shows a multi-decadal variability likely due to natural processes acting on the internal climate system variability at regional scale. A 16–30 years cycle is found in Alaska and seems to be linked to the Pacific Decadal Oscillation (PDO) whereas ~ 20–30 and ~ 50–90 years periodicities characterize the North Atlantic climate regime, likely in relation with the Atlantic Multidecadal Oscillation (AMO). These regional features are apparently linked to the sea-ice cover fluctuations through ice-temperature positive feedback.
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Abstract. Terrestrial biosphere models (TBMs) have become an integral tool for extrapolating local observations and understanding of land-atmosphere carbon exchange to larger regions. The North American Carbon Program (NACP) Multi-scale synthesis and Terrestrial Model Intercomparison Project (MsTMIP) is a formal model intercomparison and evaluation effort focused on improving the diagnosis and attribution of carbon exchange at regional and global scales. MsTMIP builds upon current and past synthesis activities, and has a unique framework designed to isolate, interpret, and inform understanding of how model structural differences impact estimates of carbon uptake and release. Here we provide an overview of the MsTMIP effort and describe how the MsTMIP experimental design enables the assessment and quantification of TBM structural uncertainty. Model structure refers to the types of processes considered (e.g. nutrient cycling, disturbance, lateral transport of carbon), and how these processes are represented (e.g. photosynthetic formulation, temperature sensitivity, respiration) in the models. By prescribing a common experimental protocol with standard spin-up procedures and driver data sets, we isolate any biases and variability in TBM estimates of regional and global carbon budgets resulting from differences in the models themselves (i.e. model structure) and model-specific parameter values. An initial intercomparison of model structural differences is represented using hierarchical cluster diagrams (a.k.a. dendrograms), which highlight similarities and differences in how models account for carbon cycle, vegetation, energy, and nitrogen cycle dynamics. We show that, despite the standardized protocol used to derive initial conditions, models show a high degree of variation for GPP, total living biomass, and total soil carbon, underscoring the influence of differences in model structure and parameterization on model estimates.