Intraseasonal to Interannual Climate Variability and Prediction
This chapter outlines a set of topics essential to become aware of the science, methods, and procedures for operational prediction in the intraseasonal to interannual (ISI) time range. The quality of ISI predictions rely on three basic capabilities: observing networks to sample the Earth’s climate system, an analysis scheme to summarize past and present observations into physically consistent time series of spatial fields, and a prediction method to project a present state of the climate system into the future. Observing networks provide essential data to estimate the true state of the climate system at regular time intervals and to measure physical climate processes and climate variability. Conventional observing networks are designed to sample the most relevant scales of variability and processes occurring in the climate system. Numerical analysis schemes generate physically consistent estimates of the state of the climate system based on observations; they vary in complexity from simple interpolation methods to modern data assimilation schemes. The dynamical approach to carry out ISI predictions use numerical schemes that couple atmosphere, land, ocean and cryosphere models. ISI predictions are also carried out using statistical methods or a combination of the two approaches. The computer burden associated with carrying out dynamical forecasts with comprehensive coupled models is high. Thus, operational centers perform those coupled model runs routinely out to a few seasons only.
Current prediction practices include running the coupled models in ensemble mode to account for the uncertainty in the forecasts and to filter out unpredictable signals through ensemble averaging. Furthermore, recognizing the difficulty for a single model to measure its own forecast limitations, it is common to combine ensembles from multiple independent models in a scheme called multi-model ensemble. The practice of incorporating reanalysis and hindcast data sets as tools to post-process raw forecast outputs considerably reduces forecast systematic errors, improves reliability, and enhances the estimation of potential skill and the detection of extreme events. The outputs of coupled global models often serve as input to downstream models such as limited-area high-resolution climate models, river routing, crop growth, and expanding the applicability of ISI forecasts to the regional and local level. Graphical interfaces that permit data analysis and smart decision support systems are becoming necessary to assist forecasters and decision-makers in their real-time endeavors.
As models become more skillful and reliable, the methods to generate climate numerical guidance have evolved from subjective approaches to increasingly objective and unsupervised numerical procedures. Nonetheless, human intervention typically increases the skill and value of the final products and is essential for product interpretation and communication to final users. Challenges to realistically model the climate system are many, but those highlighted by the scientific community include better model representation of fine-scale processes in clouds, ocean eddies, and surface interactions and feedbacks and better coupled integration of model climate components. More skillful ISI forecasts are also conditioned to greater computer resources, more extensive and strategic observing systems, and effective data assimilation and model initialization schemes for the coupled climate prediction systems.
KeywordsClimate variability ENSO MJO Teleconnections Seasonal predictions Predictability Earth system models Ocean-atmosphere coupling Surface local feedbacks Observing networks Multi-model ensembles Hindcasts Reanalysis
- M.A. Balmaseda, M.K. Davey, D.L.T. Anderson, Decadal and seasonal dependence of ENSO prediction skill. J. Clim. 8, 2705–2715 (1995). https://doi.org/10.1175/1520-0442(1995)008<2705:DASDOE>2.0.CO;2CrossRefGoogle Scholar
- T.P. Barnett, R. Preisendorfer, Origins and levels of monthly and seasonal forecast skill for United States surface air temperatures determined by canonical correlation analysis. Mon. Weather Rev. 115, 1825–1850 (1987). https://doi.org/10.1175/1520-0493(1987)115<1825:OALOMA>2.0.CO;2CrossRefGoogle Scholar
- A.G. Barnston, Linear statistical short-term climate predictive skill in the Northern Hemisphere. J. Clim. 7, 1513–1564 (1994). https://doi.org/10.1175/1520-0442(1994)007<1513:LSSTCP>2.0.CO;2CrossRefGoogle Scholar
- L.-C. Chen, K.C. Mo, Q. Zhang, J. Huang, Meteorological drought prediction using a multi-model ensemble approach, in 38th NOAA Climate Diagnostics & Prediction Workshop Special Issue, Climate Prediction S&T Digest, 2013, pp. 48–50Google Scholar
- R. Hagedorn, The rationale behind the success of multi-model ensembles in seasonal forecasting – I. Basic concepts. Tellus 57A, 219–233 (2005)Google Scholar
- B.J. Hoskins, D.J. Karoly, The steady linear response of a spherical atmosphere to thermal and orographic forcing. J. Atmos. Sci. 38, 1179–1196 (1981). https://doi.org/10.1175/1520-0469(1981)038<1179:TSLROA>2.0.CO;2CrossRefGoogle Scholar
- R.S. Lindzen, S. Nigam, On the role of the sea surface temperature gradients in forcing low-level winds and convergence in the tropics. J. Atmos. Sci. 44, 2440–2458 (1987). https://doi.org/10.1175/1520-0469(1987)044<2418:OTROSS>2.0.CO;2CrossRefGoogle Scholar
- E. Lorenz, Empirical Orthogonal Functions and Statistical Weather Prediction. Sci. Rep. No. 1, Statistical Forecasting Project, M.I.T., Cambridge, MA, 1956, 48 pp.Google Scholar
- F. Mesinger, G. DiMego, E. Kalnay, K. Mitchell, P.C. Shafran, W. Ebisuzaki, D. Jović, J. Woollen, E. Rogers, E.H. Berbery, M.B. Ek, Y. Fan, R. Grumbine, W. Higgins, H. Li, Y. Lin, G. Manikin, D. Parrish, W. Shi, North American regional reanalysis. Bull. Am. Meteorol. Soc. 87, 343–360 (2006)CrossRefGoogle Scholar
- C. Penland, P.D. Sardeshmukh, The optimal growth of tropical sea surface temperature anomalies. J. Clim. 8, 1999–2024 (1995). https://doi.org/10.1175/1520-0442(1995)008<1999:TOGOTS>2.0.CO;2CrossRefGoogle Scholar
- J. Shukla, Dynamical predictability of monthly means. J. Atmos. Sci. 38, 2547–2572 (1981). https://doi.org/10.1175/1520-0469(1981)038<2547:DPOMM>2.0.CO;2CrossRefGoogle Scholar
- H. Van den Dool, Empirical Methods in Short-Term Climate Prediction (Oxford University, Oxford, 2007)Google Scholar
- H.M. Van den Dool, A.G. Barnston, 1995: Forecasts of global sea surface temperature out to a year using the constructed analogue method, in Proceedings of the 19th Annual Climate Diagnostics Workshop, 14–18 Nov 1994, College Park, pp. 416–419Google Scholar
- J.M. Wallace, D.S. Gutzler, Teleconnections in the geopotential height field during the Northern Hemisphere winter. Mon. Weather Rev. 109, 784–812 (1981). https://doi.org/10.1175/1520-0493(1981)109<0784:TITGHF>2.0.CO;2CrossRefGoogle Scholar
- Y. Xia et al., Continental-scale water and energy flux analysis and validation for the North American Land Data Assimilation System project phase 2 (NLDAS-2): 1. Intercomparison and application of model products. J. Geophys. Res. 117, D03109 (2012a). https://doi.org/10.1029/2011JD016048CrossRefGoogle Scholar