Abstract: The North American Regional Climate Change Assessment Program (NARCCAP) is an international program to produce high resolution climate change scenarios and investigate uncertainties in regional scale projections of future climate by nesting multiple RCMs within a collection of driving AOGCMs. The resulting 60+ TB of output is intended for a wide variety of uses, including regional analysis, impacts studies, and further downscaling. This poster discusses the importance of data standardization in supporting these goals, usability insights from the 2nd NARCCAP Users Workshop, and techniques for detecting and correcting common data errors encountered in the quality-control process, as well as providing an update on NARCCAP progress and some preliminary climate-change results.
Abstract: The analysis of regional climate model (RCM) outputs frequently requires spatial interpolation of the data from the model's native grid to another set of locations: a different grid is needed for comparison with other models, a set of station locations for modeling of dependent processes or comparison with raw observations, specific points of interest for impacts studies, and so on. Different interpolation algorithms will produce results with different spatial characteristics, such as smoothness, synoptic patterning, and distribution of extremes. To explore the importance of these differences in the NARCCAP context, we regrid model output from six different RCMs driven with NCEP boundary conditions using several interpolation methods of varying mathematical sophistication: nearest-neighbor, bilinear, inverse-distance weighting, and thin-plate spline interpolation. For each algorithm, the results are compared with observations, driving data, and source model data to determine what the magnitude of the artifacts due to interpolation is and whether these effects are likely to be significant for inter-model comparison, impacts modeling and analysis, and other uses popular in the NARCCAP community.
Abstract: The analysis of regional climate model (RCM) outputs frequently requires spatial interpolation of the data from the model's native grid to another set of locations: a different grid for intermodel comparison, a set of station locations for modeling of dependent processes or comparison with raw observations, specific points of interest for impacts studies, and so on. Elevation is sometimes neglected during interpolation, even though it has a major influence on climate, with an increase of 1000 meters in altitude giving temperature changes equivalent to a shift in latitude of around 7.5 degrees poleward. The spatial scale over which elevation varies significantly is often much smaller than the scale at which RCMs are typically run (tens of kilometers), and thus the difference in elevation from one set of locations to another can be quite large, even if the locations come from two grids with comparable resolutions. Consequently, the results obtained by interpolation can, in prin ciple, be significantly different depending on whether or not one corrects for elevation. We examine the relevance and characteristics of changes due to elevation correction to determine whether it is important to users of NARCCAP data. Elevation correction in this case is performed by interpolating the data using a thin plate spline algorithm (a type of kriging) with elevation provided as a covariate field. We compare the bias against observations of data regridded with and without elevation correction for the six NARCCAP RCMs using gridded observational datasets with comparable spatial resolution (CRU and UDEL, at 1/2-degree grid spacing) and at much higher resolution (PRISM, at 4 -km grid spacing). We also consider the spatial distribution of bias and changes in bias due to elevation correction, the statistical characteristics of bias reduction, and the relationships of bias to elevation and to the observables in question. These results are evaluated in the context of intermodel comparison, impacts modeling and analysis, and other uses popular in the NARCCAP community.
Climate model output often contains significant biases that must be removed before the data is used for impacts analysis or as a forcing input for other models. We apply statistical bias correction to monthly mean surface air temperature and precipitation data from the NARCCAP output archive and examine the characteristics of the distributions of these fields on a regional basis. Bias-correcting monthly data at the regional scale is the first step in developing a methodology for bias-correcting daily data at the gridcell level, and also will provide valuable information for studies of bias in these simulations.
First, we decompose the monthly average data into climatic regions, using the Bukovsky regionalization for North America. Operating on a regional basis, we then fit an appropriate statistical distribution (gaussian for temperature and gamma for precipitation) to RCM output. Next, we bias-correct model output by using quantile mapping (QM) to create a transfer function that adjusts the distribution of model data from the current-climate simulations to match the cumulative distribution function (CDF) of observed data (from the University of Delaware half-degree gridded observational dataset) for the same period. The same transfer function is then applied to output from the future-climate simulations. Finally, we fit the corrected data with an appropriate statistical distribution.
Because the statistical distributions are characterized by two parameters, we can then summarize the biases and the changes in the distributions due to bias-correction and due to climate change in terms of changes in those parameters. We examine these changes on a regional basis for the entire North American simulation domain and throughout the annual cycle.