A Review of Downscaling Methods for Climate Change Projections (original) (raw)

Adjustment of modeled values to reflect the observed distribution and statistics. Change factor (CF): Ratio between values of current climate and future GCM simulations. Climatology: Long-term average of a given variable, often over time periods of 20 to 30 years. For example, a monthly climatology consists of a mean value for each month computed over 30 years, and a daily climatology consists of a mean value for each day. Coastal breeze: Wind in coastal areas driven by differences in the rate of cooling/warming of land and water. Convective precipitation: Intense precipitation of short duration that characterizes most of the rainfall in the tropics. Direct and indirect effect of aerosols: Atmospheric aerosols are solid and liquid particles suspended in air that influence the amount of solar radiation that reaches the surface of the Earth. Aerosols can cool the surface of the Earth via reflection of solar radiation. This is termed the direct effect. The effect of aerosols on the radiative properties of Earth's cloud cover is referred to as the indirect effect. Downscaling: Derivation of local to regional-scale (10-100 kilometers) information from larger scale modeled or observed data. There are two main approaches: dynamical downscaling and statistical downscaling. Emissions Scenario: Estimates of future greenhouse gas emissions released into the atmosphere. Such estimates are based on possible projections of economic and population growth and technological development, as well as physical processes within the climate system. Feedback (climate): An interaction within the climate system in which the result of an initial process triggers changes in a second process that in turn influences the initial one. A positive feedback intensifies the original process and a negative one reduces it. Frequency distribution: An arrangement of statistical data that shows the frequency of the occurrence of different values. General Circulation Model (GCM): A global, three-dimensional computer model of the climate system that can be used to simulate human-induced climate change. GCMs represent the effects of such factors as reflective and absorptive properties of atmospheric water vapor, greenhouse gas concentrations, clouds, annual and daily solar heating, ocean temperatures, and ice boundaries. Grid cell: A rectangular area that represents a portion of the Earth's surface. Interannual variability: Year-to year change in the mean state of the climate that is caused by a variety of factors and interactions within the climate system. One important example of interannual variability is the quasi-periodic change of atmospheric and oceanic circulation patterns in the Tropical Pacific region, collectively known as El Niño-Southern Oscillation (ENSO). A Review of Downscaling Methods for Climate Change Projections vi Interpolation: The process of estimating unknown data values that lie between known values. Various interpolation techniques exist. One of the simplest is linear interpolation, which assumes a constant rate of change between two points. Unknown values anywhere between these two points can then be assigned. Land-sea contrast: Difference in pressure and other atmospheric characteristics that arises between the land and ocean, caused by the difference in the rate of cooling/warming of their respective surfaces. Large-scale climate information: Atmospheric characteristics (e.g., temperature, precipitation, relative humidity) spanning several hundred to several thousand kilometers. Lateral boundaries: Information about the air masses, obtained from GCM output or observations, used by RCMs to derive fine-scale information. Markovian process: When values of the future depend solely on the present state of the system and not the past. Predictand: The variable that is to be predicted. In downscaling, the predictand is the local climate variable of interest. Predictor: A variable that can be used to predict the value of another variable. In downscaling, the predictor is the large-scale climate variable. Regional Climate Model (RCM): High-resolution (typically 50 kilometers) computer model that represents local features. It is constructed for limited areas, run for periods of ~20 years, and driven by large-scale data. Spatial downscaling: Refers to the methods used to derive climate information at finer spatial resolution from coarser spatial resolution GCM output. The fundamental basis of spatial downscaling is the assumption that significant relationships exist between local and large-scale climate. Spatial resolution: In climate, spatial resolution refers to the size of a grid cell in which 10-80 kilometers and 200-500 kilometers are considered to be "fine" and "coarse," respectively. Stationarity: Primary assumption of statistical downscaling; as the climate changes, the statistical relationships do not. It assumes that the statistical distribution associated with each climate variable will not change, that the same large-scale predictors will be identified as important, and that the same statistical relationships between predictors and predictands exist. Stochastic: Describes a process or simulation in which there is some indeterminacy. Even if the starting point is known, there are several directions in which the process can evolve, each with a distinct probability. Synoptic: Refers to large-scale atmospheric characteristics spanning several hundred to several thousand kilometers. Systematic bias: The difference between the observed data and modeled results that occurs due model imperfections. Temporal downscaling: Refers to the derivation of fine scale temporal data from coarser-scale temporal information (e.g., daily data from monthly or seasonal information). Its main application is in impact studies when impact models require daily or even more frequent information. Temporal resolution: The time scale at which a measurement is taken or a value is represented. Daily and monthly resolutions denote one value per day and one value per month, respectively.