• Arnell, N. W., 2011: Incorporating climate change into water resources planning in England and Wales. J. Amer. Water Resour. Assoc., 47, 541549.

    • Search Google Scholar
    • Export Citation
  • Asrar, G., , A. Busalacchi, , and J. Hurrel, 2012: Developing plans and priorities for climate science in service to society. Eos, Trans. Amer. Geophys. Union, 93, 128128.

    • Search Google Scholar
    • Export Citation
  • Christensen, J. H., and Coauthors, 2007: Regional climate projections. Climate Change 2007: The Physical Science Basis, S. Solomon et al., Eds., Cambridge University Press, 847–940.

  • Giorgi, F., 2002: Variability and trends of the sub-continental scale surface climate in the twentieth century. Part II: AOGCM simulations. Climate Dyn., 18, 675691.

    • Search Google Scholar
    • Export Citation
  • Giorgi, F., , X. Q. Bi, , and Y. Qian, 2003: Indirect vs. direct effects of anthropogenic sulfate on the climate of East Asia as simulated with a regional coupled climate-chemistry/aerosol model. Climatic Change, 58, 345376.

    • Search Google Scholar
    • Export Citation
  • Hamed, K. H., 2008: Trend detection in hydrologic data: The Mann–Kendall trend test under the scaling hypothesis. J. Hydrol., 349, 350363.

    • Search Google Scholar
    • Export Citation
  • Hamed, K. H., , and A. R. Rao, 1998: A modified Mann–Kendall trend test for autocorrelated data. J. Hydrol., 204, 182196.

  • Hegerl, G. C., and Coauthors, 2007: Understanding and attributing climate change. Climate Change 2007: The Physical Science Basis, S. Solomon et al., Eds., Cambridge University Press, 663–745.

  • Hurst, H. E., 1951: Long term storage capacities of reservoirs. Trans. Amer. Soc. Civ. Eng., 116, 776808.

  • Kendall, M. G., 1975: Rank Correlation Methods. 4th ed. Griffin, 202 pp.

  • Koutsoyiannis, D., 2000: A generalized mathematical framework for stochastic simulation and forecast of hydrologic time series. Water Resour. Res., 36, 15191533.

    • Search Google Scholar
    • Export Citation
  • Koutsoyiannis, D., 2003: Climatic change, the Hurst phenomenon, and hydrological statistics. Hydrol. Sci. J., 48, 324.

  • Koutsoyiannis, D., , and A. Montanari, 2007: Statistical analysis of hydroclimatic time series: Uncertainty and insights. Water Resour. Res., 43, W05429, doi:10.1029/2006WR005592.

    • Search Google Scholar
    • Export Citation
  • Kumar, S., , V. Merwade, , J. Kam, , and K. Thurner, 2009: Streamflow trends in Indiana: Effects of long term persistence, precipitation and subsurface drains. J. Hydrol., 374, 171183.

    • Search Google Scholar
    • Export Citation
  • Kunkel, K. E., , X.-Z. Liang, , J. Zhu, , and Y. Lin, 2006: Can CGCM simulate the twentieth-century “warming hole” in the central United States? J. Climate, 19, 41374153.

    • Search Google Scholar
    • Export Citation
  • Mandelbrot, B. B., 1965: Une classe de processus stochastiques homothetiques a soi: Application a la loi climatologique de H.E. Hurst. C. R. Acad. Sci. Paris, 260, 32743276.

    • Search Google Scholar
    • Export Citation
  • Mann, H. B., 1945: Non-parametric tests against trend. Econometrica, 13, 245259.

  • McLeod, A. I., , and K. W. Hipel, 1978: Preservation of the rescaled adjusted range: 1. A reassessment of the Hurst Phenomenon. Water Resour. Res., 14, 491508.

    • Search Google Scholar
    • Export Citation
  • Meehl, G. A., , J. M. Arblaster, , and G. Branstator, 2012: Mechanisms contributing to the warming hole and the consequent U.S. east–west differential of heat extremes. J. Climate, 25, 63946408.

    • Search Google Scholar
    • Export Citation
  • Mitchell, T. D., , and P. D. Jones, 2005: An improved method of constructing a database of monthly climate observations and associated high-resolution grids. Int. J. Climatol., 25, 693712, doi:10.1002/joc.1181.

    • Search Google Scholar
    • Export Citation
  • Morice, C. P., , J. J. Kennedy, , N. A. Rayner, , and P. D. Jones, 2012: Quantifying uncertainties in global and regional temperature change using an ensemble of observational estimates: The HadCRUT4 data set. J. Geophys. Res., 117, D08101, doi:10.1029/2011JD017187.

    • Search Google Scholar
    • Export Citation
  • NCAR, 2012: The NCAR Command Language (version 6.0.0). UCAR/NCAR/CISL/VETS. [Available online at http://www.ncl.ucar.edu/.]

  • Onoz, B., , and M. Bayazit, 2003: The power of statistical tests for trend detection. Turkish J. Eng. Environ. Sci., 27, 247251.

  • Pan, Z., , R. W. Arritt, , E. S. Takle, , W. J. Gutowski Jr., , C. J. Anderson, , and M. Segal, 2004: Altered hydrologic feedback in a warming climate introduces a “warming hole.” Geophys. Res. Lett., 31, L17109, doi:10.1029/2004GL020528.

    • Search Google Scholar
    • Export Citation
  • Pielke, R. A., Sr., and Coauthors, 2007: Unresolved issues with the assessment of multidecadal global land surface temperature trends. J. Geophys. Res., 112, D24S08, doi:10.1029/2006JD008229.

    • Search Google Scholar
    • Export Citation
  • Pielke, R. A., Sr., and Coauthors, 2011: Land use/land cover changes and climate: Modeling analysis and observational evidence. Wiley Interdiscip. Rev.: Climate Change, 2, 828850, doi:10.1002/wcc.144.

    • Search Google Scholar
    • Export Citation
  • Randall, D. A., and Coauthors, 2007: Climate models and their evaluation. Climate Change 2007: The Physical Science Basis, S. Solomon et al., Eds., Cambridge University Press, 589–662.

  • Sakaguchi, K., , X. Zeng, , and M. A. Brunke, 2012a: Temporal- and spatial-scale dependence of three CMIP3 climate models in simulating surface temperature trends in the twentieth century. J. Climate, 25, 24562470.

    • Search Google Scholar
    • Export Citation
  • Sakaguchi, K., , X. Zeng, , and M. A. Brunke, 2012b: The hindcast skill of the CMIP ensembles for the surface air temperature trend. J. Geophys. Res., 117, D16113, doi:10.1029/2012JD017765.

    • Search Google Scholar
    • Export Citation
  • Sen, P. K., 1968: Estimates of the regression coefficients based on Kendall’s tau. J. Amer. Stat. Assoc., 63, 13791389.

  • Shanahan, T. M., and Coauthors, 2009: Atlantic forcing of persistent drought in West Africa. Science, 324, 377380.

  • Taylor, K. E., , R. J. Stouffer, , and G. A. Meehl, 2012: An overview of CMIP5 and the experiment design. Bull. Amer. Meteor. Soc., 93, 485498.

    • Search Google Scholar
    • Export Citation
  • Theil, H., 1950: A rank-invariant method of linear and polynomial regression analysis. III. Proc. Ned. Akad. Wet., 53, 13971412.

  • Thorne, P. W., , D. E. Parker, , J. R. Christy, , and C. A. Mears, 2005: Uncertainties in climate trends. Bull. Amer. Meteor. Soc., 86, 14371442.

    • Search Google Scholar
    • Export Citation
  • von Storch, H., 1995: Misuses of statistical analysis in climate research. Analysis of Climate Variability: Applications of Statistical Techniques, H. von Storch and A. Navarra, Eds., Springer, 11–26.

  • Vose, R. S., , R. Helm, , R. L. Schmoyer, , T. R. Karl, , P. M. Steurer, , J. K. Eischeid, , and T. C. Peterson, 1992: The Global Historical Climatology Network: Long-term monthly temperature, precipitation, sea level pressure, and station pressure data. Rep. ORNL/CDIAC-53, Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, Oak Ridge, TN, 325 pp. [Available online at http://cdiac.ornl.gov/ftp/ndp041/ndp041.pdf.]

  • Yue, S., , P. Pilon, , B. Phinney, , and G. Cavadias, 2002: The influence of autocorrelation on the ability to detect trend in hydrological series. Hydrol. Processes, 16, 18071829.

    • Search Google Scholar
    • Export Citation
  • Zhou, T., , and R. Yu, 2006: Twentieth-century surface air temperature over China and the globe simulated by coupled climate models. J. Climate, 19, 58435858.

    • Search Google Scholar
    • Export Citation
  • Zwiers, F. W., , and H. von Storch, 1995: Taking serial correlation into account in test of mean. J. Climate, 8, 336351.

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 330 330 62
PDF Downloads 280 280 66

Evaluation of Temperature and Precipitation Trends and Long-Term Persistence in CMIP5 Twentieth-Century Climate Simulations

View More View Less
  • 1 Center for Ocean–Land–Atmosphere Studies, Calverton, Maryland
  • | 2 School of Civil Engineering, Purdue University, West Lafayette, Indiana
  • | 3 Center for Ocean–Land–Atmosphere Studies, Calverton, Maryland, and Department of Atmospheric, Oceanic and Earth Sciences, George Mason University, Fairfax, Virginia
  • | 4 Department of Agronomy, and Department of Earth, Atmospheric and Planetary Sciences, Purdue University, West Lafayette, Indiana
© Get Permissions
Restricted access

Abstract

The authors have analyzed twentieth-century temperature and precipitation trends and long-term persistence from 19 climate models participating in phase 5 of the Coupled Model Intercomparison Project (CMIP5). This study is focused on continental areas (60°S–60°N) during 1930–2004 to ensure higher reliability in the observations. A nonparametric trend detection method is employed, and long-term persistence is quantified using the Hurst coefficient, taken from the hydrology literature. The authors found that the multimodel ensemble–mean global land–average temperature trend (0.07°C decade−1) captures the corresponding observed trend well (0.08°C decade−1). Globally, precipitation trends are distributed (spatially) at about zero in both the models and in the observations. There are large uncertainties in the simulation of regional-/local-scale temperature and precipitation trends. The models’ relative performances are different for temperature and precipitation trends. The models capture the long-term persistence in temperature reasonably well. The areal coverage of observed long-term persistence in precipitation is 60% less (32% of land area) than that of temperature (78%). The models have limited capability to capture the long-term persistence in precipitation. Most climate models underestimate the spatial variability in temperature trends. The multimodel ensemble–average trend generally provides a conservative estimate of local/regional trends. The results of this study are generally not biased by the choice of observation datasets used, including Climatic Research Unit Time Series 3.1; temperature data from Hadley Centre/Climatic Research Unit, version 4; and precipitation data from Global Historical Climatology Network, version 2.

Corresponding author address: Sanjiv Kumar, Center for Ocean–Land–Atmosphere Studies, 4041 Powder Mill Rd., Suite 302, Calverton, MD 20705. E-mail: sanjiv@cola.iges.org

Abstract

The authors have analyzed twentieth-century temperature and precipitation trends and long-term persistence from 19 climate models participating in phase 5 of the Coupled Model Intercomparison Project (CMIP5). This study is focused on continental areas (60°S–60°N) during 1930–2004 to ensure higher reliability in the observations. A nonparametric trend detection method is employed, and long-term persistence is quantified using the Hurst coefficient, taken from the hydrology literature. The authors found that the multimodel ensemble–mean global land–average temperature trend (0.07°C decade−1) captures the corresponding observed trend well (0.08°C decade−1). Globally, precipitation trends are distributed (spatially) at about zero in both the models and in the observations. There are large uncertainties in the simulation of regional-/local-scale temperature and precipitation trends. The models’ relative performances are different for temperature and precipitation trends. The models capture the long-term persistence in temperature reasonably well. The areal coverage of observed long-term persistence in precipitation is 60% less (32% of land area) than that of temperature (78%). The models have limited capability to capture the long-term persistence in precipitation. Most climate models underestimate the spatial variability in temperature trends. The multimodel ensemble–average trend generally provides a conservative estimate of local/regional trends. The results of this study are generally not biased by the choice of observation datasets used, including Climatic Research Unit Time Series 3.1; temperature data from Hadley Centre/Climatic Research Unit, version 4; and precipitation data from Global Historical Climatology Network, version 2.

Corresponding author address: Sanjiv Kumar, Center for Ocean–Land–Atmosphere Studies, 4041 Powder Mill Rd., Suite 302, Calverton, MD 20705. E-mail: sanjiv@cola.iges.org
Save