PALEOcLIMATE MODELING INduDEs the development and application of numerical climate models to simulate climates through Earth's 4.6 billion-year history. Paleoclimate modeling is a theoretical branch of the natural science fields of paleoclimatology and paleoceanography, which seek to document and understand Earth's climate and ocean history. A fundamental goal of paleoclimate modeling is to determine the causes of past global climate change as a guide to modern and future climate change.
Numerical climate models are mathematical expressions of the theoretical laws and physical processes that govern the climate system, approximated and written in computer code for efficient computation. Climate models can vary tremendously in their complexity, capability, domain, and spatial resolution. Climate models used for paleo-applications range from relatively simple one-dimensional models that are based on the conservation of energy (energy balance models), to models that solve the primitive equations to predict the general circulation of the atmosphere and ocean in three dimensions (general circulation models). The capabilities of these models may be limited to one component of the Earth system, the atmosphere, for example; or may include interactions between several components, such as the atmosphere, biosphere, ocean, and cryosphere.
The model domain may be global in extent, or limited to a region, such as the western United States, and is discretized into grid cells. The horizontal (usually latitude by longitude) and vertical (usually altitude, depth, or thickness) resolution of the models can vary substantially between models. Regional models of limited domain may have a horizontal grid spacing of approximately 12 mi. (20 km.), while most global models have grid spacing on the order of hundreds of miles (kilometers). The decision to use a particular model at a specific resolution is based on the scientific problem to be addressed and consideration of the computational cost of running the model.
Paleoclimate models are nearly always initially developed and fine-tuned for the modern climate as a check on the performance of the model. If the model can successfully simulate important aspects of the modern climate, then the model may be modified for paleoclimate applications. It is often assumed that modern climate dynamics and physics are representative of past climates, and thus these aspects do not change for paleoclimate applications. The modifications for paleo-applications are frequently limited to the model's boundary conditions. In mathematics, boundary conditions define the limits of a set of partial differential equations. Similarly, in paleoclimate modeling, the boundary conditions define the limits of the numerical model. Modifications to the boundary conditions are made to represent processes that occur on geological timescales, such as continental drift and seafloor spreading, mountain building and erosion, sea-level change, evolution, and geochemical cycling.
Common boundary conditions required for global paleoclimate modeling include continental positions and topography, ocean bathymetry, vegetation distribution, soil type, solar luminosity, river drainage, atmospheric trace gases (such as carbon dioxide, methane, nitrous oxide, and ozone), and Earth's orbital characteristics (such as obliquity, precession, and eccentricity). The accurate reconstruction of these boundary conditions is a critical aspect, and one of the largest uncertainties, of paleoclimate modeling. For very ancient time periods, geological evidence of past boundary conditions may not exist or may be known only crudely. Numerous paleoclimate-model-ing studies have demonstrated that differences within the uncertainty of a boundary condition can have regional or global climatic consequences, as shown by Poulsen, et al.
Paleoclimate studies often simulate a specific interval or time slice. In this case, the boundary conditions remain constant throughout the experiment, and the model is run until a steady-state climate is achieved. The length of the integration varies, depending on the residence time of the system that is modeled. An atmosphere-only model may be run for as little as 30 years, while models that include ocean and ice-sheet components require thousands of years of model integration. The simulation of time slices is prevalent in studies that predict climate on geological timescales, since it is impractical with modern computational capabilities to run most numerical models for millions of years. Transient runs, in which the time-varying response to a forcing is of interest, have been used in studies of Quaternary climate change where the time interval of interest may be hundreds to thousands of years, as shown by Ganopolski and Rahmstorf.
Two approaches are frequently used in the application of paleoclimate models. Many paleoclimate modeling studies fall within the category of sensitivity experiments: modeling experiments in which one parameter is varied at a time and compared to a control case. Sensitivity experiments quantify the model's climate response to a single factor, such as carbon dioxide. This methodology can be used to evaluate how uncertainties in boundary conditions influence the climate prediction. Alternatively, some researchers have attempted to simulate a particular time interval by specifying the best boundary conditions, and then comparing the simulation to climate proxy reconstructions. In practice, many studies mix these two approaches.
The validation of paleoclimate models is made through comparison with climate proxies, paleo-oceanographic and geologic evidence used to infer past climate. Climate proxies differ tremendously in type, spatial and temporal distribution, and accuracy. In general, climate proxy uncertainties increase with age, while the sampling density decreases with age. Types of paleoclimate proxies include: lithologic indicators (such as tillites, evaporites, and coals), geochemical indicators (such as foraminiferal and ice 518O, foraminiferal Mg/Ca ratios), and paleo-floral indicators (such as tree-ring dendrochronology, distribution of plant types, and leaf-margin analysis). The comparison of paleoclimate models and proxies is often not direct because climate models are not usually capable of simulating geologic processes, for example, the deposition of evaporites or the incorporation of O18 in the tests of marine invertebrates. Rather, climate models produce output, such as surface temperature, rates of precipitation and evaporation, winds and currents, snow and sea ice, which are indirectly compared with climate proxies.
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