Skip to main content

Advertisement

Log in

Global seasonal climate predictability in a two tiered forecast system. Part II: boreal winter and spring seasons

  • Published:
Climate Dynamics Aims and scope Submit manuscript

    We’re sorry, something doesn't seem to be working properly.

    Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Abstract

We examine the Florida Climate Institute–Florida State University Seasonal Hindcast (FISH50) skill at a relatively high (50 km grid) resolution two tiered Atmospheric General Circulation Model (AGCM) for boreal winter and spring seasons at zero and one season lead respectively. The AGCM in FISH50 is forced with bias corrected forecast sea surface temperature averaged from two dynamical coupled ocean–atmosphere models. The comparison of the hindcast skills of precipitation and surface temperature from FISH50 with the coupled ocean–atmosphere models reveals that the probabilistic skill is nearly comparable in the two types of forecast systems (with some improvements in FISH50 outside of the global tropics). Furthermore the drop in skill in going from zero lead (boreal winter) to one season lead (boreal spring) is also similar in FISH50 and the coupled ocean–atmosphere models. Both the forecast systems also show that surface temperature hindcasts have more skill than the precipitation hindcasts and that land based precipitation hindcasts have slightly lower skill than the corresponding hindcasts over the ocean.

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

Access this article

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
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  • AchutaRao KM, Sperber KR (2006) ENSO simulation in coupled ocean-atmosphere models: are the current models better? Clim Dyn. doi:10.1007/s00382-006-0119-7

    Google Scholar 

  • Oleson KW et al (2004) Technical description of the Community Land Model (CLM). Tech. Rep. NCAR/TN-461 + STR, NCAR, Boulder, CO, 174 p

  • Kirtman BP et al (2013) The North American multi-model ensemble (NMME) for intra-seasonal to interannual prediction. Bull Am Met Soc (Submitted)

  • Saha S et al (2013) The NCEP climate forecast system version 2. J Climate (Submitted) Available from http://cfs.ncep.noaa.gov/cfsv2.info/CFSv2_paper.pdf

  • Alpert JC, Kanamitsu M, Caplan PM, Sela JG, White GH, Kalnay E (1988) Mountain induced gravity wave drag parameterization in the NMC medium-range model. Preprints, Eighth conference on numerical weather prediction, Baltimore, MD. Am Meteor Soc 726–733

  • Bacmeister J, Pegion PJ, Schubert SD, Suarez MJ (2000) An atlas of seasonal means simulated by the NSIPP 1 atmospheric GCM. Vol. 17. NASA Tech. Memo. 104606, Goddard Space Flight Center, Greenbelt, MD, 194 p

  • Barnston AG et al (1994) Long lead seasonal forecasts-where do we stand? Bull Am Meteorol Soc 75:2097–2114

    Article  Google Scholar 

  • Bengtsson L, Schlese U, Roeckner E (1993) A two-tiered approach to long-range climate forecasting. Science 261:1026–1029

    Article  Google Scholar 

  • Berger AL (1978) Long-term variations of daily insolation and quaternary climate changes. J Atmos Sci 25:2362–2367

    Article  Google Scholar 

  • Bohn TJ, Sonessa MY, Lettenmaier DP (2010) Seasonal hydrologic forecasting: do multi-model ensemble averages always yield improvements in forecast skill? J Hydrometeorol 11:1358–1372

    Article  Google Scholar 

  • Chou M-D, Suarez MJ (1994) An efficient thermal infrared radiation parameterization for use in general circulation models. In: NASA Technical report series on global modeling and data assimilation, NASA/TM-1994-104606, vol 3. Goddard Space Flight Center, Greenbelt, USA

  • Chou M-D, Lee K-T (1996) Parameterizations for the absorption of solar radiation by water vapor and ozone. J Atmos Sci 53:1203–1208

    Article  Google Scholar 

  • Chun H, Baik J (1998) Momentum flux by thermally induced interval gravity wave and its approximation for large-scale models. J Atmos Sci 55:3299–3310

    Article  Google Scholar 

  • Clark MP, Hay LE (2004) Use of medium-range numerical weather prediction model output to produce forecasts of stream-flow. J Hydrometeor 5:15–32

    Article  Google Scholar 

  • Clough SA, Shephard MW, Mlawer EJ, Delamere JS, Iacono MJ, Cady-Pereira K, Boaukabara S, Brown PD (2005) Atmospheric radiative transfter modeling: a summary of the AER codes. J Quant Spectrosc Radiat Transfer 91:233–244

    Article  Google Scholar 

  • Collins WD et al (2006a) The formulation and atmospheric simulation of the community atmosphere model version 3 (CAM3). J Climate 19:2144–2161

    Article  Google Scholar 

  • Collins WD et al (2006b) The community climate system model version 3 (CCSM3). J Climate 19:2122–2143

    Article  Google Scholar 

  • DelSole T, Shukla J (2012) Climate models produce skillful predictions of Indian summer monsoon rainfall. Geophys Res Lett 39. doi:10.1029/2012GL051279

  • DeWitt DG (2005) Retrospective forecasts of interannual sea surface temperature anomalies from 1982 to present using a directly coupled atmosphere–ocean general circulation model. Mon Wea Rev 133:2972–2995

    Article  Google Scholar 

  • Drijfhout SS, Walsteijn FH (1998) Eddy-induced heat transport in a coupled ocean–atmospheric anomaly model. J Phys Oceanogr 28:250–265

    Article  Google Scholar 

  • Ek MB, Mitchell KE, Lin Y, Rogers E, Grunmann P, Koren V, Gayno G, Tarpley JD (2003) Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational mesoscale 437 Eta model. J Geophys Res 108:8851. doi:10.1029/2002JD003296

    Article  Google Scholar 

  • Goddard L, Mason SJ, Zebiak SE, Ropelewsky CF, Basher R, Cane MA (2001) Current approaches to seasonal-to-interannual climate predictions. Int J Climatol 21:1111–1152

    Article  Google Scholar 

  • Hack JJ (1994) Parameterization of moist convection in the National Center for Atmospheric Research Community Climate Model (CCM2). J Geophys Res 99:5551–5568

    Article  Google Scholar 

  • Hoerling MP, Hurrell JW, Xu T (2001) Tropical origins for recent north Atlantic climate change. Science 292:90–92

    Article  Google Scholar 

  • Holtsalag AAM, Boville BA (1993) Local versus nonlocal boundary-layer diffusion in a global climate model. J Climate 6:1825–1842

    Article  Google Scholar 

  • Hong S-Y, Pan H-L (1996) Nonlocal boundary layer vertical diffusion in a medium-range forecast model. Mon Wea Rev 122:3–26

    Google Scholar 

  • Hong S-Y, Pan H-L (1998) Convective trigger function for a mass-flux cumulus parameterization scheme. Mon Wea Rev 126:2599–2620

    Article  Google Scholar 

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

    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. Meteor. Monogr. No. 46. Am Meteor Soc 165–170

  • Kanamitsu M et al (2002a) NCEP dynamical seasonal forecast system 2000. Bull Am Meteor Soc 83:1019–1037

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Kirtman BP (2003) The COLA anomaly coupled model: ensemble ENSO prediction. Mon Wea Rev 131:2324–2341

    Article  Google Scholar 

  • Kirtman BP, Min D (2009) Multimodel ensemble ENSO prediction with CCSM and CFS. Mon Wea Rev 137:2908–2930

    Article  Google Scholar 

  • Kirtman BP, Fan Y, Schneider EK (2002) The COLA global coupled and anomaly coupled ocean-atmosphere GCM. J Climate 15:2301–2320

    Article  Google Scholar 

  • Koster RD, Suarez MJ, Heiser M (2000) Variance and predictability of precipitation at seasonal-to-interannual time scales. J Hydrometeorol 1:26–64

    Article  Google Scholar 

  • Kumar A, Hoerling MP (1995) Prospects and limitations of seasonal atmospheric GCM predictions. Bull Am Meteorol Soc 76:335–345

    Article  Google Scholar 

  • LaRow TE (2013) The impact of SST bias correction in north Atlantic hurricane retrospective forecasts. Mon Wea Rev 141:490–498

    Article  Google Scholar 

  • Mason SJ, Graham NE (1999) Conditional probabilities, relative operating characteristics, and relative operating levels. Weather Forecast 14:713–725

    Article  Google Scholar 

  • Mason SJ, Graham NE (2002) Areas beneath the relative operating characteristics (ROC) and levels (RROL) curves: statistical significance and interpretations. Quart J R Meteorol Soc 128:2145–2166

    Article  Google Scholar 

  • McFarlane NA (1987) The effects of orographically excited gravity wave drag on the general circulation of the lower stratosphere and troposphere. J Atmos Sci 44:1775–1800

    Article  Google Scholar 

  • Misra V, Li H, Wu Z, Dinapoli S (2013) Global seasonal climate predictability in a two tiered forecast system: part I : boreal summer and fall seasons. Clim Dyn. doi:10.1007/s00382-013-1812-y

  • Mitchell TD, Jones PD (2005) An improved method of constructing a database of monthly climate observations and associated high-resolution grids. Int J Climatol 25:693–712

    Article  Google Scholar 

  • Mlawer EJ, Taubman SJ, Brown PD, Iacono MJ, Clough SA (1997) Radiative transfer for inhomogeneous atmosphere: RRTM, a validated correlated-k model for the longwave. J Geophys Res 102(D14):16663–16682

    Google Scholar 

  • Moorthi S, Suarez MJ (1992) Relaxed Arakawa-Schubert. A parameterization of moist convection for general circulation models. Mon Wea Rev 120:978–1002

    Article  Google Scholar 

  • Moura AD, Hastenrath S (2004) Climate prediction for Brazil’s Nordeste: performance of empirical and numerical modeling methods. J Climate 17:2667–2672

    Article  Google Scholar 

  • Palmer TN, Brankovi CC, Richardson DS (2000) A probability and decision-model analysis of PROVOST seasonal multi-model ensemble integrations. Quart J R Meteorol Soc 126:2013–2034

    Article  Google Scholar 

  • Ramanathan V, Downey P (1986) A nonisothermal emissivity and absorptivity formulation for water vapor. J Geophys Res 91:8649–8666

    Article  Google Scholar 

  • Ropelewski CF, Halpert MS (1986) North American precipitation and temperature patterns associated with the El Nino/Southern Oscillation (ENSO). Mon Wea Rev 114:2352–2362

    Article  Google Scholar 

  • Ropelewski CF, Halpert MS (1987) Global and regional scale precipitation patterns associated with the El Niño/Southern Oscillation. Mon Wea Rev 115:1606–1626

    Article  Google Scholar 

  • Saha S et al (2006) The NCEP climate forecast system. J Climate 19:3483–3517

    Article  Google Scholar 

  • Saha S et al (2010) The NCEP climate forecast system reanalysis. Bull Am Meteor Soc 91:1015–1057

    Article  Google Scholar 

  • Shimpo A, Kanamitsu M, Iacobellis SF, Hong S-Y (2008) Comparison of four cloud schemes in simulating the seasonal mean field forced by the observed sea surface temperature. Mon Wea Rev 136:2557–2575

    Article  Google Scholar 

  • Shukla J (1998) Predictability in the midst of chaos: a scientific basis for climate forecasting. Science 282:728–731

    Article  Google Scholar 

  • Shukla J, Anderson J, Baumhefner D, Brankovic C, Chang Y, Kalnay E, Marx L, Palmer T, Paolino DA, Ploshay J, Schubert S, Straus DM, Suarez M, Tribbia J (2000) Dynamical seasonal prediction. Bull Am Meteor Soc 81:2593–2606

    Article  Google Scholar 

  • Shukla J, Palmer TN, Hagedorn R, Hoskins B, Kinter J, Marotzke J, Miller M, Slingo J (2010) Towards a new generation of world climate research and computing facilities. Bull Am Meteor Soc 91:1407–1412

    Article  Google Scholar 

  • Smith TM, Reynolds RW, Peterson TC, Lawrimore J (2008) Improvements to NOAA’s historical merged land-ocean surface temperature analysis (1880–2006). J Climate 21:2283–2296

    Article  Google Scholar 

  • Stockdale TN, Anderson DLT, Alves JOS, Balmaseda MA (1998) Global seasonal rainfall forecasts using a coupled ocean-atmosphere model. Nature 392:370–373

    Article  Google Scholar 

  • Stockdale TN, Anderson DLT, Balmaseda MA, Doblas-Reyes FJ, Ferranti L, Mogensen K, Palmer TN, Molteni F, Vitart F (2011) ECMWF seasonal forecast system 3 and its prediction of sea surface temperature. Clim Dyn. doi:10.1007/s00382-010-0947-3

    Google Scholar 

  • Taylor KE, William D, Zwiers F (2000) The SST and seaice boundary conditions for AMIPII simulation. PCMDI report 60. http://www-pcmdi.llnl.gov/publications/ab60.html

  • Tiedtke M (1983) The sensitivity of the time-mean large-scale flow o cumulus convection in the ECMWF model. In: Proceedings ECMWF Workshop on Convection in Large-Scale Models. European Centre for Medium-Range Weather Forecasts, Shinfield Park, Reading, United Kingdom, pp 297–316

  • Wu Z, Huang NE (2009) Ensemble empirical mode decomposition: a noise-assisted data analysis method. Adv Adapt Data Anal 1:1–41

    Article  Google Scholar 

  • Wu Z, Huang NE, Chen X (2009) The multi-dimensional ensemble empirical model decomposition method. Adv Adapt Data Anal 1:272–339

    Google Scholar 

  • Xie and Arkin (1997) Global precipitation: a 17-year monthly analysis based on gauge observations, satellite estimates, and numerical model outputs. Bull Am Meteorol Soc 78:2539–2558

    Article  Google Scholar 

  • Zhang GJ, McFarlane NA (1995) Sensitivity of climate simulations to the parameterization of cumulus convection in the Canadian Climate Centre general circulation model. Atmos Ocean 33:407–446

    Article  Google Scholar 

  • Zhang S, Harrison MJ. Rosati A, Wittenberg AT (2007) System design and evaluation of coupled ensemble data assimilation for global oceanic climate studies. Mon Wea Rev 135(10). doi:10.1175/MWR3466.1

  • Zhu J, Huang B, Marx L, Kinter JL III, Balmaseda MA, Zhang R-H, Hu Z-Z (2012) Ensemble ENSO hindcasts initialized from multiple ocean analyses. Geophys Res Lett 39:L09602. doi:10.1029/2012GL051503

    Google Scholar 

Download references

Acknowledgments

This paper is dedicated to the memory of Dr. Masao Kanamitsu, without whose pioneering development of the FISH50 AGCM, this work would not have been possible. We also acknowledge the help of Dr. Zhaohua Wu who provided us the methodology and the data for the bias corrected SST (SSTOLF). We thank Mr. Steven DiNapoli for making Figs. 14, 15, 16. This work was supported by grants from NOAA (NA12OAR4310078, NA10OAR4310215, NA11OAR4310110), USGS (06HQGR0125), and USDA (027865). All computations for this paper were done on the computational resources provided by the Extreme Science and Engineering Discovery Environment (XSEDE) under TG-ATM120017 and TG-ATM120010.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Vasubandhu Misra.

Appendix: Comparison of FISH50 with the other National Multi-Model Ensemble (NMME) models

Appendix: Comparison of FISH50 with the other National Multi-Model Ensemble (NMME) models

The NMME project (Kirtman et al. 2013; http://www.cpc.ncep.noaa.gov/products/ctb/nmme/) hosted by International Research Institute for Climate and Society, Columbia University and maintained in real time at the NCEP Climate Prediction Center (http://www.cpc.ncep.noaa.gov/products/NMME/) are eight single tiered coupled ocean–atmosphere models, which have conducted extensive seasonal hindcasts over the same time period as FISH50 and more. In fact NMME models have completed seasonal hindcasts for several lead times throughout the year and here we compare the AROC for tercile events of seasonal mean surface land temperature and precipitation from FISH50 at zero (one season) lead time for JJA (SON) with the corresponding hindcasts of the NMME. The horizontal and vertical resolutions of the NMME models along with their references are shown in the Table below.

National Multi-Model Ensemble (NMME) models

Model

Horizontal resolution

Vertical resolution

References

CFSv1

T62 (~200 km)

64 sigma

Saha et al. (2006)

CFSv2

T126 (~100 km)

64 sigma-pressure

Saha et al. (2010)

CCSM3

T85 (~140 km)

26 sigma-pressure

Kirtman and Min (2009)

ECHAM-Anom

T42 (~250 km)

19 sigma-pressure

DeWitt (2005)

ECHAM-Dir

T42 (~250 km)

19 sigma-pressure

DeWitt (2005)

GFDL

2 × 2.5 degrees

24 Layers

Zhang et al. (2007)

GFDL-aer04

2 × 2.5 degrees

24 Layers

Zhang et al. (2007)

GMAO

2 × 2.5 degrees

34 Layers

Bacmeister et al. (2000)

See Figs. 14, 15, 16.

Fig. 14
figure 14

AROC averaged over global oceans for a DJF, b MAM, over tropical oceans for c DJF, and d MAM for low, middle, and upper terciles of NMME and FISH50 precipitation

Fig. 15
figure 15

AROC averaged over global land for a DJF, b MAM, over tropical land for c DJF, and d MAM for low, middle, and upper terciles of NMME and FISH50 precipitation

Fig. 16
figure 16

AROC averaged over global land for a DJF, b MAM, over tropical land for c DJF, and d MAM for low, middle, and upper terciles of NMME and FISH50 surface land temperature

Rights and permissions

Reprints and permissions

About this article

Cite this article

Li, H., Misra, V. Global seasonal climate predictability in a two tiered forecast system. Part II: boreal winter and spring seasons. Clim Dyn 42, 1449–1468 (2014). https://doi.org/10.1007/s00382-013-1813-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00382-013-1813-x

Keywords

Navigation