Skip to main content

Advertisement

SpringerLink
Log in

Evaluation of dynamically downscaled extreme temperature using a spatially-aggregated generalized extreme value (GEV) model

  • Published:
Climate Dynamics Aims and scope Submit manuscript

Abstract

The weather research and forecast (WRF) model downscaling skill in extreme maximum daily temperature is evaluated by using the generalized extreme value (GEV) distribution. While the GEV distribution has been used extensively in climatology and meteorology for estimating probabilities of extreme events, accurately estimating GEV parameters based on data from a single pixel can be difficult, even with fairly long data records. This work proposes a simple method assuming that the shape parameter, the most difficult of the three parameters to estimate, does not vary over a relatively large region. This approach is applied to evaluate 31-year WRF-downscaled extreme maximum temperature through comparison with North American regional reanalysis (NARR) data. Uncertainty in GEV parameter estimates and the statistical significance in the differences of estimates between WRF and NARR are accounted for by conducting a novel bootstrap procedure that makes no assumption of temporal or spatial independence within a year, which is especially important for climate data. Despite certain biases over parts of the United States, overall, WRF shows good agreement with NARR in the spatial pattern and magnitudes of GEV parameter estimates. Both WRF and NARR show a significant increase in extreme maximum temperature over the southern Great Plains and southeastern United States in January and over the western United States in July. The GEV model shows clear benefits from the regionally constant shape parameter assumption, for example, leading to estimates of the location and scale parameters of the model that show coherent spatial patterns.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  • Bell JL, Sloan LC, Snyder MA (2004) Regional changes in extreme climatic events: a future climate scenario. J Clim 17:81–87

    Article  Google Scholar 

  • Beniston M (2004) The 2003 heat wave in Europe: a shape of things to come? An analysis based on Swiss climatological data and model simulations. Geophys Res Lett 31:L02202. doi:10.1029/2003GL018857

    Article  Google Scholar 

  • Bowden JH, Otte TL, Nolte CG, Otte MJ (2012) Examining interior grid nudging techniques using two-way nesting in the WRF model for regional climate modeling. J Clim 25:2805–2823

    Article  Google Scholar 

  • Brown SJ, Caesar J, Ferro CTA (2008) Global changes in extreme daily temperature since 1950. J Geophys Res 113:D05115. doi:10.1029/2006JD008091

    Article  Google Scholar 

  • Bukovsky MS, Karoly DJ (2007) A brief evaluation of precipitation from the North American regional reanalysis. J Hydrometeorol 8:837–846

    Article  Google Scholar 

  • Coles SG (2001) An introduction to statistical modeling of extreme values. Springer, London

    Book  Google Scholar 

  • Cooley D, Sain S (2010) Spatial hierarchical modeling of precipitation extremes from a regional climate model. J Agric Bio Environ Stat 15(3):381–402

    Article  Google Scholar 

  • Cooley D, Nychka D, Naveau P (2007) Bayesian spatial modeling of extreme precipitation levels. J Am Stat Assoc 102:824–840

    Article  Google Scholar 

  • Craigmile PF, Guttorp P (2013) Can a regional climate model reproduce observed extreme temperatures? Statistica 73(1):103–122

    Google Scholar 

  • Daly C, Neilson RP, Phillips DL (1994) A statistical-topographic model for mapping climatological precipitation over mountainous terrain. J Appl Meteorol 33:140–158

    Article  Google Scholar 

  • Daly C, Halbleib M, Smith JI, Gibson WP, Doggett MK, Taylor GH, Curtis J, Pasteris PP (2008) Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States. Int J Climatol 28:2031–2064

    Article  Google Scholar 

  • Di Luca A, de Elía R, Laprise R (2013) Potential for added value in temperature simulated by high-resolution nested RCMs in present climate and in the climate change signal. Clim Dyn 40:443–464. doi:10.1007/s00382-012-1384-2

    Article  Google Scholar 

  • Dole R, Hoerling M, Perlwitz J, Eischeid J, Pegion P, Zhang T, Quan X-W, Xu T, Murray D (2011) Was there a basis for anticipating the 2010 Russian heat wave? Geophys Res Lett 38:L06702. doi:10.1029/2010GL046582

    Article  Google Scholar 

  • Durman CF, Gregory JM, Hassell DC, Jones RG, Murphy JM (2001) A comparison of extreme European daily precipitation simulated by a global and regional climate model for present and future climates. Q J R Meteorol Soc 127:1005–1015

    Article  Google Scholar 

  • Easterling DR et al (2000) Climate extremes: observations, modeling, and impacts. Science 289:2068–2074

    Article  Google Scholar 

  • Field CB et al (2012) Managing the risks of extreme events and disasters to advance climate change adaptation. IPCC Special Report. www.ipcc-wg2.gov/SREX/

  • Fita L, Fernández J, García-Díez M (2010) CLWRF: WRF modifications for regional climate simulation under future scenarios. In: Extended abstracts of the 11th WRF users’ workshop, Boulder, 21–25 June 2010, pp 1–4

  • Fowler HJ, Ekstrom M, Kilsby CG, Jones PD (2005) New estimates of future changes in extreme rainfall across the UK using regional climate model integrations. 1. Assessment of control climate. J Hydrol 300:212–233

    Article  Google Scholar 

  • Frei C, Scholl R, Schmidli J, Fukutome S, Vidale PL (2006) Future change of precipitation extremes in Europe: an intercomparison of scenarios from regional climate models. J Geophys Res 111:D06105. doi:10.1029/2005JD005965

    Article  Google Scholar 

  • Gao Y, Fu JS, Drake JB, Liu Y, Lamarque JF (2012) Projected changes of extreme weather events in the eastern United States based on a high resolution climate modeling system. Environ Res Lett 7(4):044025. doi:10.1088/1748-9326/7/4/044025

    Article  Google Scholar 

  • Hasan HB, Ahmad Radi NFB, Kassim SB (2012) Modeling of extreme temperature using generalized extreme value (GEV) distribution: a case study of Penang. In: Proceedings of the world congress on engineering, vol I WCE 2012, July 4–6, London

  • Hong SY, Lim JJ (2006) The WRF single-moment 6-class microphysics scheme (WSM6). J Korean Meteorol Soc 42:129–151

    Google Scholar 

  • Hosking JRM (1990) L-moments: analysis and estimation of distributions using linear combinations of order statistics. J R Stat Soc B 52:105–124

    Google Scholar 

  • Hosking JRM (1992) Moments or L moments? An example comparing two measures of distributional shape. Am Stat 46(3):186–189. doi:10.2307/2685210

    Google Scholar 

  • Hosking JRM (2006) On the characterization of distributions by their L-moments. J Stat Plan Inference 136:193–198

    Article  Google Scholar 

  • Hosking JRM, Wallis JR (1988) The effect of intersite dependence on regional flood frequency analysis. Water Resour Res 24:588–600

    Article  Google Scholar 

  • Hosking JRM, Wallis JR (1997) Regional frequency analysis: an approach based on L-moments. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • Kain JS (2004) The Kain–Fritsch convective parameterization: an update. J Appl Meteorol 43:170–181

    Article  Google Scholar 

  • Kain JS, Fritsch JM (1990) A one-dimensional entraining/detraining plume model and its application in convective parameterization. J Atmos Sci 47:2784–2802

    Article  Google Scholar 

  • Kain JS, Fritsch JM (1993) Convective parameterization for mesoscale models: the Kain–Fritsch scheme. The representation of cumulus convection in numerical models. Meteorol Monogr 24:165–170

    Google Scholar 

  • Kanamaru H, Kanamitsu M (2007) 57-year California reanalysis downscaling at 10 km (CaRD10) Part II. Comparison with North American Regional Reanalysis. J Clim 20:5553–5571

    Article  Google Scholar 

  • Kanamitsu M, Ebisuzaki W, Woollen J, Yang S-K, Hnilo JJ, Fiorino M, Potter GL (2002) NCEP–DOE AMIP-II reanalysis (R-2). Bull Am Meteorol Soc 83:1631–1643

    Article  Google Scholar 

  • Katz RW, Craigmile PF, Guttorp P, Haran M, Sansó B, Stein M (2013) Uncertainty analysis in climate change assessments. Nat Clim Change 3:769–771

    Article  Google Scholar 

  • Kharin VV, Zwiers FW (2005) Estimating extremes in transient climate change simulations. J Clim 18:1156–1173

    Article  Google Scholar 

  • Kharin VV, Zwiers FW, Zhang X, Hegerl GC (2007) Changes in temperature and precipitation extremes in the IPCC ensemble of global coupled model simulations. J Clim 20:1419–1444

    Article  Google Scholar 

  • Leadbetter MR, Lindgren G, Rootzén H (1983) Extremes and related properties of random sequences and processes. Statistics series. Springer, Berlin

    Book  Google Scholar 

  • Liu P, Tsimpidi AP, Hu Y, Stone B, Russell AG, Nenes A (2012) Differences between downscaling with spectral and grid nudging using WRF. Atmos Chem Phys 12:3601–3610

    Article  Google Scholar 

  • Loikith PC, Lintner BR, Kim J, Lee H, Neelin JD, Waliser DE (2013) Classifying reanalysis surface temperature probability density functions (PDFs) over North America with cluster analysis. Geophys Res Lett 40:3710–3714. doi:10.1002/grl.50688

    Article  Google Scholar 

  • Mannshardt E, Craigmile PF, Tingley MP (2013) Statistical modeling of extreme value behavior in North American tree-ring density series. Clim Change 117:843–858

    Article  Google Scholar 

  • Meehl GA, Tebaldi C (2004) More intense, more frequent, and longer lasting heat waves in the 21st century. Science 305:994–997

    Article  Google Scholar 

  • Meehl GA et al (2007) 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, pp 747–845

  • Mesinger F et al (2006) North American regional reanalysis. Bull Am Meteorol Soc 87:343–360

    Article  Google Scholar 

  • Miguez-Macho G, Stenchikov GL, Robock A (2004) Spectral nudging to eliminate the effects of domain position and geometry in regional climate model simulations. J Geophys Res 109:D13104. doi:10.1029/2003JD004495

    Article  Google Scholar 

  • Morrison H, Thompson G, Tatarskii V (2009) Impact of cloud microphysics on the development of trailing stratiform precipitation in a simulated squall line: comparison of one- and two-moment schemes. Mon Weather Rev 137:991–1007. doi:10.1175/2008MWR2556.1

    Article  Google Scholar 

  • Murari KK, Ghosh S, Patwardhan A, Daly E, Salvi K (2015) Intensification of future severe heat waves in India and their effect on heat stress and mortality. Reg Environ Change 15(4):569

    Article  Google Scholar 

  • Otte TL, Nolte CG, Otte MJ, Bowden JH (2012) Does nudging squelch the extremes in regional climate modeling? J Clim 25:7046–7066

    Article  Google Scholar 

  • Papalexiou SM, Koutsoyiannis D (2013) Battle of extreme value distributions: a global survey on extreme daily rainfall. Water Resour Res 49(1):187–201. doi:10.1029/2012WR012557

    Article  Google Scholar 

  • Perkins SE, Pitman AJ, Sisson SA (2009) Smaller projected increases in 20-year temperature returns over Australia in skill-selected climate models. Geophys Res Lett 36:L06710. doi:10.1029/2009GL037293

    Article  Google Scholar 

  • Pielke RA (2002) Mesoscale meteorological modeling, 2nd edn. Academic Press, San Diego

    Google Scholar 

  • Rahmstorf S, Coumou D (2011) Increase of extreme events in a warming world. Proc Natl Acad Sci 108:17905–17909

    Article  Google Scholar 

  • Schär C, Vidale PL, Lüthi D, Frei C, Häberli C, Liniger MA, Appenzeller C (2004) The role of increasing temperature variability in European summer heatwaves. Nature 427:332–336. doi:10.1038/nature02300

    Article  Google Scholar 

  • Schliep EM, Cooley D, Sain SR, Hoeting JA (2010) A comparison study of extreme precipitation from six different regional climate models via spatial hierarchical modeling. Extremes 13(2):219–239

    Article  Google Scholar 

  • Stott PA, Stone DA, Allen MR (2004) Human contribution to the European heatwave of 2003. Nature 432:610–614. doi:10.1038/nature03089

    Article  Google Scholar 

  • Tebaldi C, Hayhoe K, Arblaster JM, Meehl GA (2006) Going to the extremes: an intercomparison of model-simulated historical and future changes in extreme events. Clim Change 79:185–211

    Article  Google Scholar 

  • von Storch HL, Feser F (2000) A spectral nudging technique for dynamical downscaling purposes. Mon Weather Rev 128:3664–3673

    Article  Google Scholar 

  • Wang J, Kotamarthi VR (2013) Assessment of dynamical downscaling in near-surface fields with different spectral nudging approaches using the Nested Regional Climate Model (NRCM). J Appl Meteorol Climatol 52:1576–1591

    Article  Google Scholar 

  • Wang J, Kotamarthi VR (2014) Downscaling with a nested regional climate model in near-surface fields over the contiguous United States. J Geophys Res Atmos 119:8778–8797. doi:10.1002/2014JD021696

    Article  Google Scholar 

  • Wang J, Swati FNU, Stein ML, Kotamarthi VR (2015) Model performance in spatiotemporal patterns of precipitation: new methods for identifying value added by a regional climate model. J Geophys Res Atmos 120(4):1239–1259. doi:10.1002/2014JD022434

    Article  Google Scholar 

  • Wehner MF (2013) Very extreme seasonal precipitation in the NARCCAP ensemble: model performance and projections. Clim Dyn 40:59–80

    Article  Google Scholar 

  • Wehner MF, Smith R, Duffy P, Bala G (2010) The effect of horizontal resolution on simulation of very extreme US precipitation events in a global atmosphere model. Clim Dyn 32:241–247. doi:10.1007/s00382-009-0656-y

    Article  Google Scholar 

  • Zwiers FW, Kharin VV (1998) Changes in the extremes of the climate simulated by CCC GCM2 under CO2 doubling. J Clim 11:2200–2222

    Article  Google Scholar 

Download references

Acknowledgments

We thank all anonymous reviewers for their constructive comments and insights. This work was supported under a military interdepartmental purchase request from the Strategic Environmental Research and Development Program, RC-2242, through U.S. Department of Energy (DOE) Contract DE-AC02-06CH11357. The North American Regional Reanalysis (NARR) 3-hour surface air temperature data is downloaded from ftp.cdc.noaa.gov/Datasets/. The computational resources for the WRF simulations were provided by the DOE-supported Argonne Leadership Computing Facility and the National Energy Research Scientific Computing Center (NERSC, contract No. DE-AC02-05CH11231).

Author information

Authors and Affiliations

  1. Environmental Science Division, Bldg. 240, Rm. 6A22, Argonne National Laboratory, 9700 South Cass Ave., Argonne, IL, 60439, USA

    Jiali Wang

  2. Department of Statistics, University of Chicago, Chicago, IL, USA

    Yuefeng Han & Michael L. Stein

  3. Environmental Science Division, Argonne National Laboratory, Argonne, IL, USA

    Veerabhadra R. Kotamarthi

  4. Department of Statistics, Purdue University, West Lafayette, IN, USA

    Whitney K. Huang

Authors
  1. Jiali Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yuefeng Han
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Michael L. Stein
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Veerabhadra R. Kotamarthi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Whitney K. Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jiali Wang.

Additional information

The submitted manuscript has been created by UChicago Argonne, LLC, Operator of Argonne National Laboratory (“Argonne”). Argonne, a U.S. Department of Energy Office of Science laboratory, is operated under Contract No. DE-AC02-06CH11357. The U.S. government retains for itself, and others acting on its behalf, a paid-up, nonexclusive, irrevocable worldwide license in said article to reproduce, prepare derivative works, distribute copies to the public, and perform publicly and display publicly, by or on behalf of the Government.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (PDF 2652 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, J., Han, Y., Stein, M.L. et al. Evaluation of dynamically downscaled extreme temperature using a spatially-aggregated generalized extreme value (GEV) model. Clim Dyn 47, 2833–2849 (2016). https://doi.org/10.1007/s00382-016-3000-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00382-016-3000-3

Keywords

Search

Navigation