NARCCAP Model Skill and Bias for the Southeast United States


This paper investigates dynamically downscaled regional climate model (RCM) output from the North American Regional Climate Change Assessment Program (NARCCAP) for two sub-regions of the Southeast United States. A suite of four statistical measures were used to assess model skill and biases were presented in hindcasting daily minimum and maximum temperature and mean precipitation during a historical reference period, 1970-1999. Most models demonstrated high skill for temperature during the historical period. Two outliers included two RCMs run using the Geophysical Fluids Dynamics Lab (GFDL) model as their lateral boundary conditions; these models suffered from a cold maximum temperature bias. Improvement with GFDL-based projections of maximum temperature was noted from May through November when they ran with observed seasurface conditions (GFDL-timeslice), particularly for the east sub-region. Precipitation skill proved mixed-relatively high when measured using a probability density function overlap measurement or the index of agreement, but relatively low when measured with root-mean square error or mean absolute error, because several models overestimated the frequency of extreme precipitation events.

Share and Cite:

Kabela, E. and Carbone, G. (2015) NARCCAP Model Skill and Bias for the Southeast United States. American Journal of Climate Change, 4, 94-114. doi: 10.4236/ajcc.2015.41009.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] Mearns, L.O., Gutowski, W., Jones, R., Leung, R., McGuinnis, S., Nunes, A. and Qian, Y. (2009) A Regional Climate Change Assessment Program for North America. EOS Transactions of the American Geophysical Union, 90, 311-312.
[2] Vorosmarty, C.J., Green, P., Salisbury, J. and Lammers, R.B. (2000) Global Water Resources: Vulnerability from Climate Change and Population Growth. Science, 289, 284-288.
[3] Tubiello, F.N., Soussana, J.-F. and Howden, S.M. (2007) Crop and Pasture Response to Climate Change. Proceedings of the National Academy of Sciences of the United states of America, 104, 19686-19690.
[4] Kruijt, B., Witte, J.-P.M. Witte, Jacobs, C.M.J. and Kroon, T. (2008) Effects of Rising Atmospheric CO2 on Evapotranspiration and Soil Moisture: A Practical Approach for the Netherlands. Journal of Hydrology, 349, 257-267.
[5] Shem, W.O., Mote, T.L. and Shepard, J.M. (2010) Validation of NARCCAP Climate Products for Forest Resource Applications in the Southeast United States. 18th Conference on Applied Climatology, Session 10, American Meteorological Society.
[6] Smith, J.B., Vogel, J.M. and Cromwell III, J.E. (2009) An Architecture for Government Action on Adaptation to Climate Change. An Editorial Comment. Climatic Change, 95, 53-61.
[7] Brooks, N., Adger, W.N. and Kelly, P.M. (2005) The Determinants of Vulnerability and Adaptive Capacity at the National Level and the Implications for Adaptation. Global Environmental Change, 15, 151-163.
[8] Dessai, S., Goulden, M., Hulme, M., Lorenzoni, I., Nelson, D.R., Naess, L.O., Wolfe, J. and Wreford, A. (2009) Are There Social Limits to Adaptation to Climate Change? Climatic Change, 93, 335-354.
[9] Pielke, R.A., Prins, G., Rayner, S. and Sarewitz, D. (2007) Climate Change 2007: Lifting the Taboo on Adaptation. Nature, 445, 597-598.
[10] Cutter, S., Osman-Elasha, B., Campbell, J., Cheong, S.-M., McCormick, S., Pulwarty, R. and Ziervogel, G. (2012) Managing the Risks from Climate Extremes at the Local Level. Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation. A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change (IPCC), Cambridge University Press, Cambridge.
[11] Seneviratne, S.I., Nicholls, N., Easterling, D., Goodess, C.M., Kanae S., Kossin, J., Luo, Y., Marengo, J., McInnes, K., Rahimi, M., Reichstein, M., Sorteberg, A., Vera, C. and Zhang, X. (2012) Changes in Climate Extremes and Their Impacts on the Natural Physical Environment. Managing the Risks of Extreme Events and Distasters to Advance Climate Change Adaptation. A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change (IPCC), Cambridge University Press, Cambridge.
[12] Deque, M., Rowell, D.P., Luthi, D., Giorgi, F., Christensen, J.H., Rockel, B., Jacob, D., Kjellstrom, E., de Castro, M. and van den Hurk, B. (2007) An Intercomparison of Regional Climate Simulations for Europe: Assessing Uncertainties in Model Projections. Climatic Change, 81, 53-70.
[13] Jacob, D., Barring, L., Christensen, O.B., Christensen, J.H., de Castro, M., Deque, M., Giorgi, F., Hagemann, S., Hirschi, M., Jones, R., Kjellstrom, E., Lenderink, G., Rockel, B., Sanchez, E., Schar, C., Seneviratne, S.I., Somot, S., van Ulden, A. and van den Hurk, B. (2007) An Inter-Comparison of Regional Climate Models for Europe: Model Performance in Present-Day Climate. Climatic Change, 81, 31-52.
[14] Giorgi, F. (2006) Regional Climate Modeling: Status and Perspectives. Journal de Physique IV France, 139, 101-118.
[15] Sobolowski, S. and Pavelsky, T. (2012) Evaluation of Present and Future North American Regional Climate Change Assessment Program (NARCCAP) Regional Climate Simulations over the Southeast United States. Journal of Geophysical Research, 117, D01101.
[16] Schliep, E.M., Cooley, D., Sain, S.R. and Hoeting, J.A. (2010) A Comparison Study of Extreme Precipitation from Six Different Regional Climate Models Via Spatial Hierarchical Modeling. Extremes, 13, 219-239.
[17] Bukovsky, M.S. (2011) Masks for the Bukovsky Regionalization of North America. Regional Integrated Sciences Collective, Institute for Mathematics Applied to Geosciences, National Center for Atmospheric Research, Boulder.
[18] Kjellstrom, E., Boberg, F., Castro, M., Christensen, J.H., Nikulin, G. and Sanchez, E. (2010) Daily and Monthly Temperature and Precipitation Statistics as Performance Indicators for Regional Climate Models. Climate Research, 44, 135-150.
[19] Kanamitsu, M., Ebisuzaki, W., Woollen, J., Yang, S.-K., Hnilo, J.J., Fiorino, M., and Potter and G.L. (2002) NCEP-DOE AMIP-II Reanalysis (R-2). Bulletin of the American Meteorological Society, 83, 1631-1643.
[20] Nakicenovic, N. and Swart, R., Eds. (2000) Special Report on Emissions Scenarios. A Special Report of Working Group III of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge.
[21] IPCC (2007) Climate Change 2007: The Physical Basis. Cambridge University Press, Cambridge.
[22] Collins, W.D., et al. (2006) The Community Climate System Model: CCSM3. Journal of Climate, 19, 2122-2143.
[23] Flato, G.M. (2005) The Third Generation Coupled Global Climate Model (CGCM3).
[24] GFDL GAMDT (The GFDL Global Model Development Team) (2004) The New GFDL Global Atmospheric and Land Model AM2-LM2: Evaluation with Prescribed SST Simulations. Journal of Climate, 17, 4641-4673.
[25] Maurer, E.P., Wood, A.W., Adam, J.C., Lattenmaier, D.P. and Nijssen, B. (2002) A Long-Term Hydrologically-Based Dataset of Land Surface Fluxes and States for the Conterminous United States. Journal of Climate, 15, 3237-3251.<3237:ALTHBD>2.0.CO;2
[26] Maurer, E.P., O’Donnell, G.M., Lattenmaier, D.P. and Roads, J.O. (2001) Evaluation of the Land Surface Water Budget in NCEP/NCAR and NCEP/DOE Reanalyses Using an Off-Line Hydrologic Model. Journal of Geophysical Research, 106, 17841-17862.
[27] Maurer, E.P., Nijssen, B. and Lattenmaier, D.P. (2000) Use of the Reanalysis Land Surface Water Budget Variables in Hydrolic Studies. GEWEX News, 10, 6-8.
[28] Perkins, S.E., Pitman, A.J., Holbrook, N.J. and McAneney, J. (2007) Evaluation of the AR4 Climate Model’s Simulated Daily Maximum Temperature, Minimum Temperature, and Precipitation Over Australia Using Probability Density Functions. Journal of Climate, 20, 4356-4376.
[29] Boberg, F., Berg, P., Thejill, P., Gutowski, W.J. and Christensen, J.H. (2010) Improved Confidence in Climate Change Projections of Precipitation Further Evaluated Using Daily Statistics from ENSEMBLES Models. Climate Dynamics, 35, 1509-1520.
[30] Easterling, W.E., Weiss, A., Hays, C.J. and Mearns, L.O. (1998) Spatial Scales of Climate Information for Simulating Wheat and Maize Productivity: The Case of the US Great Plains. Agricultural and Forest Meteorology, 90, 51-63.
[31] Jha, M., Pan, Z., Takle, E.S. and Gu, R. (2004) Impacts of Climate Change on Streamflow in the Upper Mississippi River Basin: A Regional Climate Model Perspective. Journal of Geophysical Research, 109, D09105.
[32] Willmott, C.J., Robeson, S.M. and Matsuura, K. (2012) Short Communication: A Refined Index of Model Performance. International Journal of Climatology, 32, 2088-2094.
[33] Brankovic, C. and Palmer, T.N. (1997) Atmospheric Seasonal Predictability and Estimates of Ensemble Site. Monthly Weather Review, 125, 859-874.<0859:ASPAEO>2.0.CO;2
[34] Koo, G.-S., Boo, K.-O. and Kwon, W.-T. (2009) Projections of Temperature over Korea Using an MM5 Regional Climate Simulation. Climate Research, 40, 241-248.
[35] Jupp, T.E., Cox, P.M., Ramming, A., Thonicke, K., Lucht, W. and Cramer, W. (2010) Development of Probability Density Functions for Future South America Rainfall. New Phytologist, 187, 682-693.
[36] Dai, A. (2001) Global Precipitation and Thunderstorm Frequencies. Part I: Seasonal and Interannual Variations. Journal of Climate, 14, 1092-1111.<1092:GPATFP>2.0.CO;2
[37] Sun, Y., Solomon, S., Dai, A. and Portman, R.W. (2006) How Often Does It Rain? Journal of Climate, 19, 916-934.
[38] Maxino, C.C., McAvaney, B.J., Pitman, A.J. and Perkins, S.E. (2008) Ranking the AR4 Climate Models over the Murray-Darling Basin Using Simulated Maximum Temperature, Minimum Temperature and Precipitation. International Journal of Climatology, 28, 1097-1112.
[39] Pitman, A.J. and Perkins, S.E. (2009) Global and Regional Comparison of Daily 2-m and 1000-hPa Maximum and Minimum Temperatures in Three Global Reanalyses. Journal of Climate, 22, 4667-4681.
[40] Perkins, S.E. (2009) Smaller Projected Increases in 20-Year Temperature Returns over Australia in Skill-Selected Climate Models. Geophysical Research Letters, 36, L06710.
[41] Perkins, S.E., Irving, D.B., Brown, J.R., Power, S.B., Moise, A.F., Colman, R.A. and Smith, I. (2012) CMIP3 Ensemble Climate Projections over the Western Tropical Pacific Based on Model Skill. Climate Research, 51, 35-58.
[42] Willmott, C.J. and Wicks, D.E. (1980) An Empirical Method for the Spatial Interpolation of Monthly Precipitation Within California. Physical Geography, 1, 59-73.
[43] Willmott, C.J. (1981) On the Validation of Models. Physical Geography, 2, 184-194.
[44] Nash, J.E. and Sutcliffe, J.V. (1970) River Flow Forecasting Through Conceptual Models Part I: A Discussion of Prinicples. Journal of Hydrology, 10, 282-290.
[45] Watterson, I.G. (1996) Non-Dimensional Measures of Climate Model Performance. International Journal of Climatology, 16, 379-391.
[46] Legates, D.R. and McCabe Jr., G.J. (1999) Evaluating the Use of “Goodness-of-Fit” Measures in Hydrologic and Hydroclimatic Model Validation. Water Resources Research, 35, 233-241.
[47] Mielke, P.W. and Berry, K.J. (2001) Permutation Methods: A Distance Function Approach. Springer-Verlag, New York.
[48] Murphy, A.H. and Epstein, E.S. (1989) Skill Scores and Correlation Coefficients in Model Verification. Monthly Weather Review, 117, 572-581.<0572:SSACCI>2.0.CO;2
[49] Huffman, G.J. (1997) Estimates of Root-Mean-Square Random Error for Finite Samples of Estimated Precipitation. Journal of Applied Meteorology, 36, 1191-1201.<1191:EORMSR>2.0.CO;2
[50] Yang, Z. and Arritt, R.W. (2002) Test of Perturbed Physics Ensemble Approach for Regional Climate Modeling. Journal of Climate, 15, 2881-2896.<2881:TOAPPE>2.0.CO;2
[51] Wu, H., Hubbard, K.G. and You, J. (2005) Some Concerns When Using Data from the Cooperative Weather Station Networks: A Nebraska Case Study. Journal of Atmospheric and Oceanic Technology, 22, 592-602.
[52] Wilks, D.S. (2006) Statistical Methods in the Atmospheric Sciences. 2nd Edition, Academic Press, London.
[53] Liu, M., Kim, Y.-J. and Zhao, Q. Zhao (2012) Numerical Experiments of an Advanced Radiative Transfer Model in the U.S. Navy Operational Global Atmospheric Prediction System. Journal of Applied Meteorology and Climatology, 51, 554-570.
[54] vonStorch, H. and Zwiers, F.W. Zwiers (1999) Statistical Analysis in Climate Research. Cambridge University Press, New York.
[55] Stull, R.B. (2000) Meteorology for Scientists and Engineers. 2nd Edition, Brooks/Cole Thomson Learning, Pacific Grove.
[56] Willmott, C.J. and Matsuura, K. (2005) Advantages of the Mean Absolute Error (MAE) over the Root Mean Square Error (RMSE) in Assessing Average Model Performance. Climate Research, 30, 79-82.
[57] Freidenreich, S.M. and Ramaswamy, V. (2011) Analysis of the Biases in the Downward Shortwave Surface Flux in the GFDL CM2.1 General Circulation Model. Journal of Geophysical Research, 116, D08208.
[58] Randall, D.A., Wood, R.A., Bony, S., Colman, R., Fichefet, T., Fyfe, J., Kattsov, V., Pitman, A., Shukla, J., Srinvasan, J., Stouffer, R. J., Sumi, A. and Taylor, K.E. (2007) Climate Models and Their Evaluation. Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge.
[59] John, V.O. and Soden, B.J. (2007) Temperature and Humidity Biases in Global Climate Models and Their Impact on Climate Feedbacks. Geophysical Research Letters, 34.
[60] Novick, K.A., Oren, R., Stoy, P.C., Siqueira, M.B.S. and Katul, G.G. (2009) Nocturnal Evaportranspiration in Eddy-Covariance Records from Three Co-Located Ecosystems in the Southeastern U.S.: Implications for Annual Fluxes. Agricultural and Forest Meteorology, 149, 1491-1504.
[61] Trenberth, K.E. (2008) The Impact of Climate Change and Variablility on Heavy Precipitation, Floods, and Droughts, The Encycolpedia of Hydrological Sciences. John Wiley & Sons, Ltd., Chichester.
[62] Solman, S.A., Nunez, M.N. and Cabre, M.F. (2008) Regional Climate Change Experiments over Southern South America. I: Present Climate. Climate Dynamics, 30, 533-552.
[63] Min, S.-K., Zhang, X., Zwiers, F.W. and Hegerl, G.C. (2011) Human Contribution to More-Intense Precipitation Extremes. Nature, 470, 378-381.
[64] Samenow, J. (2012) U.S. Had Most Extreme Precipitation on Record in 2011. The Washington Post, 12 January 2012.

Copyright © 2023 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.