PLANIT CABINET VISION SOLID 2012 R2-LND
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PLANIT CABINET VISION SOLID 2012 R2-LND
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Coupled climate models can be applied on century time scales, to provide estimates of the steric (temperature and salinity effects on sea water density) and ocean dynamical (ocean circulation) components of sea level change, both globally and regionally. However, the glacier and ice sheet component are calculated off-line based on temperature and precipitation changes. In the AR5 report, changes in the SMB of glaciers and ice sheet were calculated from the global surface air temperature. In addition, GCMs also resolve climate variability related to changes in precipitation and evaporation. These changes are used to calculate short duration sea level changes (Cazenave and Cozannet, 2014363; Hamlington et al., 2017364). With various degrees of success those models capture ENSO, PDO and other modes of variability (e.g., Yin et al., 2009; Zhang and Church, 2012365), which affect sea level through redistributions of energy and salt in the ocean on slightly longer time scales. Off-line temperature and precipitation fields can be dynamically or statistically downscaled to match the high spatial resolution required for ice sheets and glaciers, but serious limitations remain. This deficiency limits adequate representation of potentially important feedbacks between changes in ice sheet geometry and climate, for example through fresh water and iceberg production that impact on ocean circulation and sea ice, which can have global consequences (Lenaerts et al., 2016366; Donat-Magnin et al., 2017367). Another limitation is the lack of coupling with the solid Earth which controls the ice sheet evolution (Whitehouse et al., 2019368). Dynamics of the interaction of ice streams with bedrock and till at the ice base remain difficult to model due to lack of direct observations. Nevertheless, several new ice sheet models have been generated over the last few years, particularly for Antarctica (Section 126.96.36.199) focusing on the dynamic contribution of the ice sheet to sea level change, which remains the key uncertainty in future projections (Church et al., 2013), particularly beyond 2050 (Kopp et al., 2014370; Nauels et al., 2017b371; Slangen et al., 2017a372; Horton et al., 2018373).
Time horizon and uncertainty: The long-term commitment to SLR (Section 188.8.131.52) and the large and deep uncertainty about the magnitude and timing of SLR beyond 2050 (Section 184.108.40.206.2), challenge standard planning and decision making practises for several reasons (high confidence; Peters et al., 2017; Pot et al., 2018; Hall et al., 2019; Hinkel et al., 2019). The time horizon of SLR extends beyond usual political, electoral and budget cycles. Furthermore, many planning and decision making practices strive for predictability and certainty, which is at odds with the dynamic risk and deep uncertainty characterising SLR (Hall et al., 2019). Tensions can arise between established risk-based planning that seeks to measure risk, and adaptation responses that embrace uncertainty and complexity (Kuklicke and Demeritt, 2016; Carlsson Kanyama et al., 2019). For example, tensions arise because of the mismatch between the relative inflexibility of existing law and institutions and the evolving nature of SLR risk and impacts (Cosens et al., 2017; Craig et al., 2017; DeCaro et al., 2017). Possible limits of in situ responses to ongoing SLR (e.g., protection and accommodation), bring into question prevailing legal approaches to property rights and land use regulation (Byrne, 2012). In addition, because uncertainty about SLR makes it difficult to decide when to wait and when to act, public actors fear being held accountable for misjudgments (Kuklicke and Demeritt, 2016). The long time horizon and uncertainty of SLR make it difficult to mobilise political will and the leadership required to take visionary action (Cuevas et al., 2016; Gibbs, 2016; Yusuf et al., 2016; Yusuf et al., 2018b).
Land use or spatial planning has the potential to help communities prepare for the future and decide how to manage coastal activities and land use taking into account the uncertainty, complexity and contestation that characterise SLR (high confidence; Hurlimann and March, 2012; Hurlimann et al., 2014; Berke and Stevens, 2016; King et al., 2016; Reiblich et al., 2017) Planners work with governing authorities, the private sector, and local communities to integrate and apply tailor-made decision analysis, public participation and conflict resolution approaches that can be institutionalised in statutory provisions, and aligned with informal institutional structures and processes carried out at various scales (Hurlimann and March, 2012; Smith and Glavovic, 2014; Berke and Stevens, 2016).
Planning can play an important role in crafting SLR responses, addressing several of the governance challenges identified above (Section 4.4.3). Planning is future focused and can assist communities to develop and pursue a shared vision, and understand and address SLR concerns in locality-specific ways (Hurlimann and March, 2012; Berke and Stevens, 2016). Planning can help articulate and clarify roles and responsibilities through statutory planning provisions, complemented by non-statutory processes (Vella et al., 2016). It can build social and administrative networks that mobilise cross-scale SLR responses, and facilitate integration of diverse mitigation and adaptation goals alongside other public aspirations and policy imperatives (Hurlimann and March, 2012; Vella et al., 2016). Planning can also facilitate the establishment of collaborative regional forums that cross jurisdictional boundaries and assist local governments and other stakeholders to pool resources and coordinate roles and responsibilities across multiple governance levels, such as the Southeast Florida Regional Climate Change Compact, USA (Shi et al., 2015; Vella et al., 2016). Regulatory planning can be used by governing authorities to steer future infrastructure, housing, industry and related development away from areas exposed to SLR (Hurlimann and March, 2012; Hurlimann et al., 2014; Smith and Glavovic, 2014; Berke and Stevens, 2016). 350c69d7ab