Seamless Precipitation Prediction Skill in the Tropics and Extratropics from a Global Model

Hongyan Zhu Centre for Australian Weather and Climate Research, Melbourne, Australia

Search for other papers by Hongyan Zhu in
Current site
Google Scholar
PubMed
Close
,
Matthew C. Wheeler Centre for Australian Weather and Climate Research, Melbourne, Australia

Search for other papers by Matthew C. Wheeler in
Current site
Google Scholar
PubMed
Close
,
Adam H. Sobel Department of Applied Physics and Applied Mathematics, Department of Earth and Environmental Sciences, Lamont-Doherty Earth Observatory, Columbia University, New York, New York

Search for other papers by Adam H. Sobel in
Current site
Google Scholar
PubMed
Close
, and
Debra Hudson Centre for Australian Weather and Climate Research, Melbourne, Australia

Search for other papers by Debra Hudson in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

The skill with which a coupled ocean–atmosphere model is able to predict precipitation over a range of time scales (days to months) is analyzed. For a fair comparison across the seamless range of scales, the verification is performed using data averaged over time windows equal in length to the lead time. At a lead time of 1 day, skill is greatest in the extratropics around 40°–60° latitude and lowest around 20°, and has a secondary local maximum close to the equator. The extratropical skill at this short range is highest in the winter hemisphere, presumably due to the higher predictability of winter baroclinic systems. The local equatorial maximum comes mostly from the Pacific Ocean, and thus appears to be mostly from El Niño–Southern Oscillation (ENSO). As both the lead time and averaging window are simultaneously increased, the extratropical skill drops rapidly with lead time, while the equatorial maximum remains approximately constant, causing the equatorial skill to exceed the extratropical at leads of greater than 4 days in austral summer and 1 week in boreal summer. At leads longer than 2 weeks, the extratropical skill flattens out or increases, but remains below the equatorial values. Comparisons with persistence confirm that the model beats persistence for most leads and latitudes, including for the equatorial Pacific where persistence is high. The results are consistent with the view that extratropical predictability is mostly derived from synoptic-scale atmospheric dynamics, while tropical predictability is primarily derived from the response of moist convection to slowly varying forcing such as from ENSO.

Corresponding author address: Dr. Matthew Wheeler, CAWCR/Bureau of Meteorology, GPO Box 1289, Melbourne, VIC 3001, Australia. E-mail: m.wheeler@bom.gov.au

Abstract

The skill with which a coupled ocean–atmosphere model is able to predict precipitation over a range of time scales (days to months) is analyzed. For a fair comparison across the seamless range of scales, the verification is performed using data averaged over time windows equal in length to the lead time. At a lead time of 1 day, skill is greatest in the extratropics around 40°–60° latitude and lowest around 20°, and has a secondary local maximum close to the equator. The extratropical skill at this short range is highest in the winter hemisphere, presumably due to the higher predictability of winter baroclinic systems. The local equatorial maximum comes mostly from the Pacific Ocean, and thus appears to be mostly from El Niño–Southern Oscillation (ENSO). As both the lead time and averaging window are simultaneously increased, the extratropical skill drops rapidly with lead time, while the equatorial maximum remains approximately constant, causing the equatorial skill to exceed the extratropical at leads of greater than 4 days in austral summer and 1 week in boreal summer. At leads longer than 2 weeks, the extratropical skill flattens out or increases, but remains below the equatorial values. Comparisons with persistence confirm that the model beats persistence for most leads and latitudes, including for the equatorial Pacific where persistence is high. The results are consistent with the view that extratropical predictability is mostly derived from synoptic-scale atmospheric dynamics, while tropical predictability is primarily derived from the response of moist convection to slowly varying forcing such as from ENSO.

Corresponding author address: Dr. Matthew Wheeler, CAWCR/Bureau of Meteorology, GPO Box 1289, Melbourne, VIC 3001, Australia. E-mail: m.wheeler@bom.gov.au
Save
  • Alves, O., and Coauthors, 2003: POAMA: Bureau of Meteorology operational coupled model seasonal forecast system. Science for Drought—Proceedings of the National Drought Forum, R. Stone and I. Partridge, Eds. Department of Primary Industries, 49–56.

  • Baldwin, M. P., and T. J. Dunkerton, 1999: Propagation of the Arctic Oscillation from the stratosphere to the troposphere. J. Geophys. Res., 104 (D24), 30 93730 946.

    • Search Google Scholar
    • Export Citation
  • Baldwin, M. P., D. B. Stephenson, D. W. J. Thompson, T. J. Dunkerton, A. J. Charlton, and A. O’Neill, 2003: Stratospheric memory and skill of extended-range weather forecasts. Science, 301, 636640.

    • Search Google Scholar
    • Export Citation
  • Bluestein, H. B., 1993: Observations and Theory of Weather Systems. Vol. II, Synoptic–Dynamic Meteorology in Midlatitudes, Oxford University Press, 606 pp.

    • Search Google Scholar
    • Export Citation
  • Boer, G. J., 1995: Analyzed and forecast large-scale tropical divergent flow. Mon. Wea. Rev.,123, 3539–3553.

  • Bolvin, D. T., R. F. Adler, G. J. Huffman, E. J. Nelkin, and J. P. Poutiainen, 2009: Comparison of GPCP monthly and daily precipitation estimates with high-latitude gauge observations. J. Appl. Meteor. Climatol., 48, 18431857.

    • Search Google Scholar
    • Export Citation
  • Bretherton, C. S., P. N. Blossey, and M. Khairoutdinov, 2005: An energy-balance analysis of deep convective self-aggregation above uniform SST. J. Atmos. Sci., 62, 42734292.

    • Search Google Scholar
    • Export Citation
  • Charney, J. G., 1947: The dynamics of long waves in a baroclinic westerly current. J. Meteor., 4, 136162.

  • Charney, J. G., 1963: A note on large-scale motions in the tropics. J. Atmos. Sci., 20, 607609.

  • Charney, J. G., 1969: A further note on large-scale motions in the tropics. J. Atmos. Sci., 26, 182185.

  • Charney, J. G., and J. Shukla, 1981: Predictability of monsoons. Monsoon Dynamics, J. Lighthill and R. P. Pearce, Eds., Cambridge University Press, 99–109.

  • Cohen, J., and D. Entekhabi, 1999: Eurasian snow cover variability and Northern Hemisphere climate variability. Geophys. Res. Lett., 26, 345348.

    • Search Google Scholar
    • Export Citation
  • Cottrill, A., and Coauthors, 2013: Seasonal forecasting in the Pacific using the coupled model POAMA-2. Wea. Forecasting, 28, 668680.

    • Search Google Scholar
    • Export Citation
  • DelSole, T., and M. K. Tippett, 2009: Average predictability time. Part II: Seamless diagnoses of predictability on multiple time scales. J. Atmos. Sci., 66, 11881204.

    • Search Google Scholar
    • Export Citation
  • DeWeaver, E., and S. Nigam, 2004: On the forcing of ENSO teleconnections by anomalous heating and cooling. J. Climate, 17, 32253235.

  • Ebert, E. E., 2001: Ability of a poor man’s ensemble to predict the probability and distribution of precipitation. Mon. Wea. Rev., 129, 24612480.

    • Search Google Scholar
    • Export Citation
  • Ebert, E. E., U. Damrath, W. Ergen, and M. E. Baldwin, 2003: The WGNE assessment of short-term quantitative precipitation forecasts. Bull. Amer. Meteor. Soc., 84, 481492.

    • Search Google Scholar
    • Export Citation
  • Ebert, E. E., and Coauthors, 2013: Progress and challenges in forecast verification. Meteor. Appl., 20, 130139.

  • Goddard, L., S. J. Mason, S. E. Zebiak, C. F. Ropelewski, R. Basher, and M. A. Cane, 2001: Current approaches to seasonal-to-interannual climate predictions. Int. J. Climatol., 21, 11111152.

    • Search Google Scholar
    • Export Citation
  • Hamill, T. M., and J. Juras, 2006: Measuring forecast skill: Is it real skill or is it the varying climatology? Quart. J. Roy. Meteor. Soc., 132, 29052923.

    • Search Google Scholar
    • Export Citation
  • Hendon, H. H., and M. L. Salby, 1994: The life cycle of the Madden–Julian oscillation. J. Atmos. Sci., 51, 22252237.

  • Hoerling, M. P., and A. Kumar, 2002: Atmospheric response patterns associated with tropical forcing. J. Climate, 15, 21842203.

  • Holland, M. M., E. Blanchard-Wrigglesworth, J. Kay, and S. Vavrus, 2013: Initial-value predictability of Antarctic sea ice in the Community Climate System Model 3. Geophys. Res. Lett., 40, 21212124, doi:10.1002/grl.50410.

    • Search Google Scholar
    • Export Citation
  • Hudson, D., O. Alves, H. H. Hendon, and G. Wang, 2011: The impact of atmospheric initialisation on seasonal prediction of tropical Pacific SST. Climate Dyn., 36, 11551171.

    • Search Google Scholar
    • Export Citation
  • Hudson, D., A. G. Marshall, Y. Yin, O. Alves, and H. H. Hendon, 2013: Improving intraseasonal prediction with a new ensemble generation strategy. Mon. Wea. Rev., 141, 44294449.

    • Search Google Scholar
    • Export Citation
  • Huffman, G. J., R. F. Adler, M. Morrissey, D. T. Bolvin, S. Curtis, R. Joyce, B. McGavock, and J. Susskind, 2001: Global precipitation at one-degree daily resolution from multi-satellite observations. J. Hydrometeor., 2, 3650.

    • Search Google Scholar
    • Export Citation
  • Kiladis, G. N., and H. F. Diaz, 1989: Global climatic anomalies associated with extremes in the Southern Oscillation. J. Climate, 2, 10691090.

    • Search Google Scholar
    • Export Citation
  • Koster, R. D., and M. J. Suarez, 2003: Impact of land surface initialization on seasonal precipitation and temperature prediction. J. Hydrometeor., 4, 408423.

    • Search Google Scholar
    • Export Citation
  • Kumar, A., and M. P. Hoerling, 1998: Annual cycle of Pacific–North American seasonal predictability associated with different phases of ENSO. J. Climate, 11, 32953308.

    • Search Google Scholar
    • Export Citation
  • L’Heureux, M. L., and D. W. J. Thompson, 2006: Observed relationships between the El Niño–Southern Oscillation and the extratropical zonal-mean circulation. J. Climate, 19, 276287.

    • Search Google Scholar
    • Export Citation
  • Lim, E.-P., H. H. Hendon, O. Alves, Y. Yin, M. Zhao, G. Wang, D. Hudson, and G. Liu, 2009: Impact of SST bias correction on prediction of ENSO and Australian winter rainfall. CAWCR Research Letters, No. 3, BoM, Melbourne, Victoria, Australia, 22–29. [Available online at http://www.cawcr.gov.au/publications/researchletters.php.]

  • Lim, E.-P., H. H. Hendon, O. Alves, Y. Yin, G. Wang, D. Hudson, M. Zhao, and L. Shi, 2010: Dynamical seasonal prediction of tropical Indo-Pacific SST and Australian rainfall with improved ocean initial conditions. CAWCR Tech. Rep. 32, 26 pp. [Available online at http://cawcr.gov.au/publications/technicalreports.php.]

  • Lim, E.-P., H. H. Hendon, and H. Rashid, 2013: Seasonal predictability of the southern annular mode due to its association with ENSO. J. Climate, 26, 80378054.

    • Search Google Scholar
    • Export Citation
  • Lorenz, E. N., 1955: Available potential energy and the maintenance of the general circulation. Tellus, 2, 157167.

  • Lorenz, E. N., 1969: Three approaches to atmospheric predictability. Bull. Amer. Meteor. Soc., 50, 345349.

  • Mapes, B. E., 1993: Gregarious tropical convection. J. Atmos. Sci., 50, 20262037.

  • Marshall, A. G., D. Hudson, M. C. Wheeler, H. H. Hendon, and O. Alves, 2011: Assessing the simulation and prediction of rainfall associated with the MJO in the POAMA seasonal forecast system. Climate Dyn., 37, 2129–2141, doi:10.1007/s00382-010-0948-2.

    • Search Google Scholar
    • Export Citation
  • Marshall, A. G., D. Hudson, M. C. Wheeler, H. H. Hendon, and O. Alves, 2012: Simulation and prediction of the southern annular mode and its influence on Australian intra-seasonal climate in POAMA. Climate Dyn., 38, 2483–2502, doi:10.1007/s00382-011-1140-z.

    • Search Google Scholar
    • Export Citation
  • Marshall, A. G., D. Hudson, H. H. Hendon, M. J. Pook, O. Alves, and M. C. Wheeler, 2013: Simulation and prediction of blocking in the Australian region and its influence on intra-seasonal rainfall in POAMA-2. Climate Dyn., doi:10.1007/s00382-013-1974-7, in press.

    • Search Google Scholar
    • Export Citation
  • McBride, J. L., and N. Nicholls, 1983: Seasonal relationships between Australian rainfall and the Southern Oscillation. Mon. Wea. Rev., 111, 19982004.

    • Search Google Scholar
    • Export Citation
  • McBride, J. L., M. R. Haylock, and N. Nicholls, 2003: Relationships between the Maritime Continent heat source and the El Niño–Southern Oscillation phenomenon. J. Climate, 16, 29052914.

    • Search Google Scholar
    • Export Citation
  • Parkinson, C. L., and D. J. Cavalieri, 2008: Arctic sea ice variability and trends, 1979–2006. J. Geophys. Res., 113, C07003, doi:10.1029/2007JC004558.

    • Search Google Scholar
    • Export Citation
  • Polvani, L. M., and P. J. Kushner, 2002: Tropospheric response to stratospheric perturbations in a relatively simple general circulation model. Geophys. Res. Lett., 29 (7), doi:10.1029/2001GL014284.

    • Search Google Scholar
    • Export Citation
  • Roff, G., D. W. J. Thompson, and H. H. Hendon, 2011: Does increasing model stratospheric resolution improve extended-range forecast skill? Geophys. Res. Lett., 38, L05809, doi:10.1029/2010GL046515.

    • Search Google Scholar
    • Export Citation
  • Schumacher, C., and R. A. Houze Jr., 2003: Stratiform rain in the tropics as seen by the TRMM Precipitation Radar. J. Climate, 16, 17391756.

    • Search Google Scholar
    • Export Citation
  • Shukla, J., 1989: Tropical forecasting: Predictability perspective. Aust. Meteor. Mag., 37, 141153.

  • Simmonds, I., and P. Hope, 1997: Persistence characteristics of Australian rainfall anomalies. Int. J. Climatol., 17, 597613.

  • Sobel, A. H., 2012: Tropical weather. Nat. Educ. Knowl., 3 (12), 2.

  • Stockdale, T. N., 1997: Coupled ocean–atmosphere forecasts in the presence of climate drift. Mon. Wea. Rev., 125, 809818.

  • Uppala, S. M., and Coauthors, 2005: The ERA-40 Re-Analysis. Quart. J. Roy. Meteor. Soc., 131, 29613012.

  • Vitart, F., 2004: Monthly forecasting at ECMWF. Mon. Wea. Rev., 132, 27612779.

  • Waliser, D., and Coauthors, 2006: The Experimental MJO Prediction Project. Bull. Amer. Meteor. Soc., 87, 425431.

  • Wang, G., D. Hudson, Y. Yin, O. Alves, H. Hendon, S. Langford, G. Liu, and F. Tseitkin, 2011: POAMA-2 SST skill assessment and beyond. CAWCR Research Letters, No. 6, BoM, Melbourne, Victoria, Australia, 40–46. [Available online at http://www.cawcr.gov.au/publications/researchletters.php.]

  • Weare, B. C., 1987: Relationships between monthly precipitation and SST variations in the tropical Pacific region. Mon. Wea. Rev., 115, 26872698.

    • Search Google Scholar
    • Export Citation
  • Weatherly, J. W., 2004: Sensitivity of Antarctic precipitation to sea ice concentrations in a general circulation model. J. Climate, 17, 32143223.

    • Search Google Scholar
    • Export Citation
  • Wheeler, M. C., 2008: Seasonal climate summary Southern Hemisphere (summer 2007-08): Mature La Niña, an active MJO, strongly positive SAM, and highly anomalous sea-ice. Aust. Meteor. Mag., 57, 379393.

    • Search Google Scholar
    • Export Citation
  • Wheeler, M. C., and G. N. Kiladis, 1999: Convectively coupled equatorial waves: Analysis of clouds and temperature in the wavenumber–frequency domain. J. Atmos. Sci., 56, 374399.

    • Search Google Scholar
    • Export Citation
  • Yin, Y., O. Alves, and P. R. Oke, 2011: An ensemble ocean data assimilation system for seasonal prediction. Mon. Wea. Rev., 139, 786808.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 4351 3030 195
PDF Downloads 856 205 7