Hydrogeology Science

 Climate change refers to a change in the state of the climate that can be identified (e.g. using statistical tests) by changes in the mean and/or the variability of its properties and that persists for an extended period, typically decades or longer. It refers to any change in climate over time, whether due to natural variability or as a result of human activity. Greenhouse gases are assumed to drive much of the contemporary climate change, and global atmospheric CO₂ concentration is the primary indicator of greenhouse gases,as well as a primary regulator of global climate

Intergovemental Panel on Climate Change (IPCC) establishes different scenario emissions of greenhouse gases namely IPCC SRES Scenarios. These IPCC SRES Scenarios are defined for different social, technological, economic, demographic, and environmental developments, which are labeled A1 (A1B, A1T, A1FI), B1, A2, and B2. The A1 storyline assumes a world of very rapid economic growth, a global population that peaks in mid-century and rapid introduction of new and more efficient technologies. A1 is divided into three groups that describe alteative directions of technological change: fossil intensive (A1FI), non-fossil energy resources (A1T) and a balance across all sources (A1B). B1 describes a convergent world, with the same global population as A1, but with more rapid changes in economic structures toward a service and information economy. B2 describes a world with intermediate population and economic growth, emphasizing local solutions to economic, social, and environmental sustainability. A2 describes a very heterogeneous world with high population growth, slow economic development and slow technological change. 

Global general circulation models (GCMs) are the most advanced tools currently available for simulating the response of the climate system to increasing greenhouse gas (GHG) concentrations under IPCC SRES Scenarios. A GCM is 3-dimensional simulation of the general circulation of the atmosphere and ocean, including representations of the land surface and snow and ice, derived from fundamental physical laws (such as Newton’s laws of motion). A coupled AOGCM typically has a horizontal resolution of between 250 and 600 km, 10 to 20 vertical layers in the atmosphere and sometimes as many as 30 layers in the oceans. 

GCMs cannot provide information at scales finer than their computational grid (typically of the order of 250 km), and processes at these unresolved scales are important. Thus, the usefulness of the raw output from a GCM for climate change assessment in specific regions is limited. To bridge the spatial resolution gaps for GCMs to produce realistic local climate projections, downscaling techniques are usually applied to the GCM output; therefore downscaling addresses the disparity between the coarse spatial scales of GCMs and observations from local meteorological stations.  Downscaling techniques divide in two groups: (a) dynamic climate modeling and (b) empirical statistical downscaling. This technique involves nesting a higher resolution Regional Climate Model (RCM) within a coarser resolution GCM. RCMs use the GCM to define time-varying atmospheric boundary conditions around a finite domain from which the physical dynamics of the atmosphere are modeled using horizontal grid spacing of about 20–50 km or less. RCMs are attractive to those seeking process understanding and causative simulation, but most downscaling is currently empirical. Statistical downscaling uses a statistically-based model to determine a relationship between observed local climate variables such as precipitation and temperature (known as predictands) and large-scale climate variables, GCM outputs, (referred to as predictors). The derived relationships between the predictors and predictands are applied on similar predictors from GCM simulations in the statistical model to estimate the corresponding local or regional climate characteristics.