This study investigates the skill of linear methods for downscaling provincial-scale precipitation over Indonesia from fields that describe the large-scale circulation and hydrological cycle. The study is motivated by the strong link between large-scale variations in the monsoon and the El Niño – Southern Oscillation (ENSO) phenomenon and regional precipitation, and the subsequent impact of reg...

This study uses an empirical downscaling method based on self-organizing maps (SOMs) to produce highresolution, downscaled precipitation projections over the state of Pennsylvania in the mid-Atlantic region of the United States for the future period 2046–65. To examine the sensitivity of precipitation change to the water vapor increase brought by global warming, the authors test the following t...

The availability of water surface inundation with high spatial resolution is of fundamental importance in several applications such as hydrology, meteorology and ecology. Medium spatial resolution sensors, like MODerate-resolution Imaging Spectroradiometer (MODIS), exhibit a significant potential to study inundation dynamics over large areas because of their high temporal resolution. However, t...

To assess a species' vulnerability to climate change, we commonly use mapped environmental data that are coarsely resolved in time and space. Coarsely resolved temperature data are typically inaccurate at predicting temperatures in microhabitats used by an organism and may also exhibit spatial bias in topographically complex areas. One consequence of these inaccuracies is that coarsely resolved...

We present probabilistic projections of 21st century climate change over Northern Eurasia using the Massachusetts Institute of Technology (MIT) Integrated Global System Model (IGSM), an integrated assessment model that couples an Earth system model of intermediate complexity with a two-dimensional zonal-mean atmosphere to a human activity model. Regional climate change is obtained by two downsc...

Downscaling of climate model data is essential to local and regional impact analysis. We compare two methods of statistical downscaling to produce continuous, gridded time series of precipitation and surface air temperature at a 1/8-degree (approximately 140 km2 per grid cell) resolution over the western U.S. We use NCEP/NCAR Reanalysis data from 1950–1999 as a surrogate General Circulation Mod...

[1] Accounting for small‐scale spatial heterogeneity of soil moisture ( ) is required to enhance the predictive skill of land surface models. In this paper, we present the results of the development, calibration, and performance evaluation of a downscaling model based on multifractal theory using aircraft‐based (800 m) estimates collected during the southern Great Plains experiment in 1997 (SGP...

There are two main approaches to downscale global climate projections: dynamical and statistical downscaling. Both families have been widely evaluated, but intercomparison studies between the scarce, usually limited temperature precipitation. In this work, we present a comparison downscaling model (SDM) based on machine learning six regional models (RCMs) from EURO-CORDEX, for five variables of...

A key challenge in climate modeling is the assessment of the impact of global climate variables on regional weather measurements such as temperature and precipitation. This assessment is usually done by downscaling the output of (coarse resolution) global climate models to regional (high resolution) predictions. There are two independent downscaling pathways: dynamic and statistical. Dynamic do...

One of the most important Limitation General Circulation Models , Large scale are being simulation of climatic variables. So should With Various method are downscaled, The ability to have identified a study area. Choose a suitable GCM model for the study area Very important role In the simulation  parameter (precipitation) is intended for future. In this research of CMIP5 Models Contains BCC-CS...