Global Sea Surface Temperature Forecasts Using a Pairwise Dynamic Combination Approach

Journal of Climate - Tập 24 Số 7 - Trang 1869-1877 - 2011
Shahadat Chowdhury1, Ashish Sharma1
1School of Civil and Environmental Engineering, University of New South Wales, Sydney, New South Wales, Australia

Tóm tắt

Abstract This paper dynamically combined three multivariate forecasts where spatially and temporally variant combination weights are estimated using a nearest-neighbor approach. The case study presented combines forecasts from three climate models for the period 1958–2001. The variables of interest here are the monthly global sea surface temperature anomalies (SSTA) at a 5° × 5° latitude–longitude grid, predicted 3 months in advance. The forecast from the static weight combination is used as the base case for comparison. The forecasted sea surface temperature using the dynamic combination algorithm offers consistent improvements over the static combination approach for all seasons. This improved skill is achieved over at least 93% of the global grid cells, in four 10-yr independent validation segments. Dynamically combined forecasts reduce the mean-square error of the SSTA by at least 25% for 72% of the global grid cells when compared against the best-performing single forecast among the three climate models considered.

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Tài liệu tham khảo

Barnston, 2003, Multimodel ensembling in seasonal climate forecasting at IRI, Bull. Amer. Meteor. Soc., 84, 1783, 10.1175/BAMS-84-12-1783

Bottomley, 1990, Global Ocean Surface Temperature Atlas “GOSTA.”

Chambers, 1992, Linear models

Chowdhury, 2009, Mitigating predictive uncertainty in hydroclimatic forecasts: Impact of uncertain inputs and model structural form

Chowdhury, 2009, Long-range Niño-3.4 predictions using pairwise dynamic combinations of multiple models, J. Climate, 22, 793, 10.1175/2008JCLI2210.1

Chowdhury, 2009, Multisite seasonal forecast of arid river flows using a dynamic model combination approach, Water Resour. Res., 45, W10428, 10.1029/2008WR007510

Colman, 2003, Statistical prediction of global sea-surface temperature anomalies, Int. J. Climatol., 23, 1677, 10.1002/joc.956

DelSole, 2009, Artificial skill due to predictor screening, J. Climate, 22, 331, 10.1175/2008JCLI2414.1

Fraedrich, 1989, Combining predictive scheme in long-range forecasting, J. Climate, 2, 291, 10.1175/1520-0442(1989)002<0291:CPSILR>2.0.CO;2

Hastie, 2001, The Elements of Statistical Learning: Data Mining, Inference, and Prediction

Huerta, 2007, Time-varying models for extreme values, Environ. Ecol. Stat., 14, 285, 10.1007/s10651-007-0014-3

Lall, 1996, A nearest neighbor bootstrap for time series resampling, Water Resour. Res., 32, 679, 10.1029/95WR02966

Lundberg, 2000, Time-varying smooth transition autoregressive models

Madec, 1997, OPA version 8 ocean general circulation model reference manual

Mehrotra, 2006, Conditional resampling of hydrologic time series using multiple predictor variables: A K-nearest neighbour approach, Adv. Water Resour., 29, 987, 10.1016/j.advwatres.2005.08.007

Palmer, 2004, Development of a European Multi-Model Ensemble System for Seasonal to Inter-Annual Prediction (DEMETER)

Peña, 2008, Consolidation of multimodel forecasts by ridge regression: Application to Pacific sea surface temperature, J. Climate, 21, 6521, 10.1175/2008JCLI2226.1

Peng, 2002, An analysis of multimodel ensemble predictions for seasonal climate anomalies, J. Geophys. Res., 107, 4710, 10.1029/2002JD002712

Raftery, 2005, Using Bayesian model averaging to calibrate forecast ensembles, Mon. Wea. Rev., 133, 1155, 10.1175/MWR2906.1

Reynolds, 1995, A high-resolution global sea surface temperature climatology, J. Climate, 8, 1571, 10.1175/1520-0442(1995)008<1571:AHRGSS>2.0.CO;2

Robertson, 2004, Improved combination of multiple atmospheric GCM ensembles for seasonal prediction, Mon. Wea. Rev., 132, 2732, 10.1175/MWR2818.1

Sanders, 1963, On subjective probability forecasting, J. Appl. Meteor., 2, 191, 10.1175/1520-0450(1963)002<0191:OSPF>2.0.CO;2

Sharma, 2000, Seasonal to interannual rainfall probabilistic forecasts for improved water supply management: Part 1 — A strategy for system predictor identification, J. Hydrol., 239, 232, 10.1016/S0022-1694(00)00346-2

Smith, 2003, Extended reconstruction of global sea surface temperatures based on COADS data (1854–1997), J. Climate, 16, 1495, 10.1175/1520-0442-16.10.1495

Thompson, 1977, How to improve accuracy by combining independent forecast, Mon. Wea. Rev., 105, 228, 10.1175/1520-0493(1977)105<0228:HTIABC>2.0.CO;2

Van den Dool, 2000, Constructed analogue prediction of the east central tropical Pacific SST and the entire World Ocean for 2001

Van den Dool, 2003, Performance and analysis of the constructed analogue method applied to U.S. soil moisture over 1981–2001, J. Geophys. Res., 108, 8617, 10.1029/2002JD003114

West, 1997, Bayesian Forecasting and Dynamic Models

Wolff, 1997, The Hamburg Ocean Primitive Equation Model (HOPE)