Search Results

You are looking at 1 - 10 of 3,111 items for :

  • Statistical downscaling x
  • Refine by Access: All Content x
Clear All
Lauren E. Hay, Jacob LaFontaine, and Steven L. Markstrom

projections are required, based on either statistical or dynamical downscaling techniques. An overview of statistical and dynamical GCM downscaling techniques for hydrologic modeling is presented in Fowler et al. ( Fowler et al. 2007 ). Statistical downscaling uses empirical relations between features reliably simulated by a GCM at gridbox scales and surface predictands at subgrid scales. Dynamical downscaling uses simulations from regional climate models with initial and lateral boundary conditions from

Full access
David W. Pierce, Daniel R. Cayan, and Bridget L. Thrasher

-spatial-scale structure using the original coarse-resolution fields along with finer-scale observations, topography, and dynamics. There are two main types of downscaling: dynamical, which uses regional climate models driven at the domain boundaries by global climate model output, and statistical, which uses the historic relationships between large- and small-scale conditions. Dynamical methods can capture nonstationary relationships between the large- and finescale that may develop in the future and can produce more

Full access
S. C. Pryor and J. T. Schoof

1. Introduction Downscaling (or right scaling) of climate information is conducted primarily to advance understanding of processes affecting climate that are not resolved well by global models and/or provide decision-makers responsible for adaptation actions with salient and credible information about possible climate change within their specific region/locality of interest ( Giorgi 2019 ; Gutowski et al. 2020 ; Maraun and Widmann 2018 ). Empirical–statistical downscaling (ESD) methods are

Free access
Megan C. Kirchmeier, David J. Lorenz, and Daniel J. Vimont

1. Introduction Downscaling allows high-resolution information to be extracted from large-scale models, particularly under future scenarios for which no observational data are available. There are two main classes of downscaling: statistical and dynamical. Dynamical downscaling typically employs the use of a regional climate model (RCM) embedded within, or driven by output from, a larger-scale global model. Giorgi (2006) presents a more complete description of RCMs and dynamical downscaling

Full access
Lauren E. Hay, Steven L. Markstrom, and Christian Ward-Garrison

greenhouse gases on the global climate system ( Alley et al. 2007 ). GCM simulations of future climate through 2099 project a wide range of possible scenarios ( Alley et al. 2007 ) but often overlook numerous climatological details necessary for hydrologic modeling at the basin scale because of their coarse resolution ( Wigley et al. 1990 ; Carter et al. 1994 ; Xu 1999 ). Statistical or dynamical methods can be used to downscale information from coarse-resolution GCMs to the basin scale for hydrologic

Full access
Seoyeon Lee and Kwang-Yul Kim

from the difficulty of prescribing open boundary conditions ( Giorgi 1990 ; Jones et al. 1995 ; Christensen et al. 1997 ; Marchesiello et al. 2001 ). A regional climate model (RCM) simulation is often inadvertently affected in a significant manner by the natural variability in a GCM output introduced through open boundary conditions. Statistical downscaling is also common and is a simple alternative to dynamical downscaling ( Hewitson and Crane 1996 ; Wilby and Wigley 1997 ; Wilby et al. 1998

Full access
Aneesh Goly, Ramesh S. V. Teegavarapu, and Arpita Mondal

techniques. Many downscaling models have been developed in the past few decades, which all have strengths and weaknesses. Several research papers offer discussions on the various methodologies, the most notable among them being the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report ( Solomon et al. 2007 ; Wigley 2004) . Statistical downscaling, which is the main emphasis in this paper, derives a statistical or empirical relationship between the large-scale climate features

Full access
Stephen R. Sobie and Trevor Q. Murdock

resolution ( Zhang et al. 2011 ). Increasing daily climate simulation spatial resolution to serve this purpose can be accomplished via postprocessing methods incorporating weather station observations such as interpolation (e.g., kriging, bilinear), constructed statistical models (e.g., Daymet; Thornton et al. 1997 ), or multisite weather generators ( Wilks 2009 ). Alternatively, regional climate models (RCMs) offer the ability to dynamically downscale meteorological variables at varying resolutions

Full access
Tobias Sauter and Victor Venema

1. Introduction Statistical downscaling has become a well-established tool in regional and local impact assessments over the past few years. Precipitation downscaling is especially paramount for impact studies to correct the spatial and temporal structure of precipitation from coarse models. To resolve the scale discrepancy between global circulation models (GCMs) and local-scale weather, a vast number of algorithms and methods have been proposed and extensively tested. A comprehensive review

Full access
J. M. Gutiérrez, A. S. Cofiño, R. Cano, and M. A. Rodríguez

historic climate data led to the development of statistical techniques. These statistical downscaling methods work with climatological databases of observations (e.g., precipitation, wind speed, and temperature) from a representative number of stations (gauges or sites) within the area of study. These observations are statistically related to the gridded atmospheric patterns, leading to forecast models for adapting a gridded forecast to local climates in a straightforward way. For instance, given a

Full access