scholarly journals Global Sea Surface Temperature Forecasts Using a Pairwise Dynamic Combination Approach

2011 ◽  
Vol 24 (7) ◽  
pp. 1869-1877 ◽  
Author(s):  
Shahadat Chowdhury ◽  
Ashish Sharma
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Climate Models ◽  
Nearest Neighbor ◽  
Sea Surface ◽  
Base Case ◽  
Grid Cells ◽  
Combination Approach ◽  
Global Grid ◽  
Global Sea Surface Temperature

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.

Global Sea Surface Temperature Forecasts Using an Improved Multimodel Approach

2014 ◽  
Vol 27 (10) ◽  
pp. 3505-3515 ◽  
Author(s):  
Mohammad Zaved Kaiser Khan ◽  
Rajeshwar Mehrotra ◽  
Ashish Sharma ◽  
A. Sankarasubramanian
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Climate Models ◽  
Sea Surface ◽  
Strongly Correlated ◽  
Multimodel Ensemble ◽  
Space And Time ◽  
Combining Forecasts ◽  
Grid Points ◽  
Global Sea Surface Temperature

Abstract With the availability of hindcasts or real-time forecasts from a number of coupled climate models, multimodel ensemble forecasting systems have gained popularity in recent years. However, many models share similar physics or modeling processes, which may lead to similar (or strongly correlated) forecasts. Assigning equal weights to each model in space and time may result in a biased forecast with narrower confidence limits than is appropriate. Although methods for combining forecasts that take into consideration differences in model accuracy over space and time exist, they suffer from a lack of consideration of the intermodel dependence that may exist. This study proposes an approach that considers the dependence among models while combining multimodel ensemble forecast. The approach is evaluated by combining sea surface temperature (SST) forecasts from five climate models for the period 1960–2005. The variable of interest, the monthly global sea surface temperature anomalies (SSTA) at a 5° × 5° latitude–longitude grid, is predicted three months in advance using the proposed algorithm. Results indicate that the proposed approach offers consistent and significant improvements for all the seasons over the majority of grid points compared to the case in which the dependence among the models is ignored. Consequently, the proposed approach of combining multiple models, taking into account the interdependence that exists, provides an attractive strategy to develop improved SST forecasts.

scholarly journals CMIP6 model-based analog forecasting for the seasonal prediction of sea surface temperature in the offshore area of China

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Weiying Peng ◽  
Quanliang Chen ◽  
Shijie Zhou ◽  
Ping Huang
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Climate Models ◽  
Global Climate Models ◽  
Sea Surface ◽  
Seasonal Forecasts ◽  
Initial State ◽  
Initial Field ◽  
Offshore Area ◽  
Model Based

AbstractSeasonal forecasts at lead times of 1–12 months for sea surface temperature (SST) anomalies (SSTAs) in the offshore area of China are a considerable challenge for climate prediction in China. Previous research suggests that a model-based analog forecasting (MAF) method based on the simulations of coupled global climate models provide skillful climate forecasts of tropical Indo-Pacific SSTAs. This MAF method selects the model-simulated cases close to the observed initial state as a model-analog ensemble, and then uses the subsequent evolution of the SSTA to generate the forecasts. In this study, the MAF method is applied to the offshore area of China (0°–45°N, 105°–135°E) based on the simulations of 23 models from phase 6 of the Coupled Model Intercomparison Project (CMIP6) for the period 1981–2010. By optimizing the key factors in the MAF method, we suggest that the optimal initial field for the analog criteria should be concentrated in the western North Pacific. The multi-model ensemble of the optimized MAF prediction using these 23 CMIP6 models shows anomaly correlation coefficients exceeding 0.6 at the 3-month lead time, which is much improved relative to previous SST-initialized hindcasts and appears practical for operational forecasting.

scholarly journals Wavelet Analysis of Seasonal Rainfall Variability of the Upper Blue Nile Basin, Its Teleconnection to Global Sea Surface Temperature, and Its Forecasting by an Artificial Neural Network

2014 ◽  
Vol 142 (5) ◽  
pp. 1771-1791 ◽  
Author(s):  
Mohamed Helmy Elsanabary ◽  
Thian Yew Gan
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
River Basin ◽  
Principal Component ◽  
Sea Surface ◽  
Seasonal Rainfall ◽  
Blue Nile ◽  
Artificial Neural ◽  
Mean Square Errors ◽  
Global Sea Surface Temperature

Abstract Rainfall is the primary driver of basin hydrologic processes. This article examines a recently developed rainfall predictive tool that combines wavelet principal component analysis (WPCA), an artificial neural networks-genetic algorithm (ANN-GA), and statistical disaggregation into an integrated framework useful for the management of water resources around the upper Blue Nile River basin (UBNB) in Ethiopia. From the correlation field between scale-average wavelet powers (SAWPs) of the February–May (FMAM) global sea surface temperature (SST) and the first wavelet principal component (WPC1) of June–September (JJAS) seasonal rainfall over the UBNB, sectors of the Indian, Atlantic, and Pacific Oceans where SSTs show a strong teleconnection with JJAS rainfall in the UBNB (r ≥ 0.4) were identified. An ANN-GA model was developed to forecast the UBNB seasonal rainfall using the selected SST sectors. Results show that ANN-GA forecasted seasonal rainfall amounts that agree well with the observed data for the UBNB [root-mean-square errors (RMSEs) between 0.72 and 0.82, correlation between 0.68 and 0.77, and Hanssen–Kuipers (HK) scores between 0.5 and 0.77], but the results in the foothills region of the Great Rift Valley (GRV) were poor, which is expected since the variability of WPC1 mainly comes from the highlands of Ethiopia. The Valencia and Schaake model was used to disaggregate the forecasted seasonal rainfall to weekly rainfall, which was found to reasonably capture the characteristics of the observed weekly rainfall over the UBNB. The ability to forecast the UBNB rainfall at a season-long lead time will be useful for an optimal allocation of water usage among various competing users in the river basin.

scholarly journals Seasonal Covariability of Dryness or Wetness in China and Global Sea Surface Temperature

2020 ◽  
Vol 33 (2) ◽  
pp. 727-747
Author(s):  
Chunxiang Li ◽  
Chunzai Wang ◽  
Tianbao Zhao
Keyword(s):  
Indian Ocean ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
El Niño ◽  
El Nino ◽  
Sea Surface ◽  
Interdecadal Variability ◽  
Coupled Modes ◽  
Indian Ocean Basin Mode ◽  
Global Sea Surface Temperature

AbstractSeasonal covariability of the dryness/wetness in China and global sea surface temperature (SST) is investigated by using the monthly self-calibrated Palmer drought severity index (PDSI) data and other data from 1950 to 2014. The singular value decomposition (SVD) analysis shows two recurring PDSI–SST coupled modes. The first SVD mode of PDSI is associated with the warm phases of the eastern Pacific–type El Niño–Southern Oscillation (ENSO), the interdecadal Pacific oscillation (IPO) or Pacific decadal oscillation (PDO), the Indian Ocean basin mode (IOBM) in the autumn and winter, and the cold phase of the IOBM in the spring. Meanwhile, the Atlantic multidecadal oscillation (AMO) pattern appears in every season except the autumn. The second SVD mode of PDSI is accompanied by a central Pacific–type El Niño developing from the winter to autumn over the tropical Pacific and a positive phase of IPO or PDO from the winter to summer. Moreover, an AMO pattern is observed in all seasons except the summer, whereas the SST over the tropical Indian Ocean shows negligible variations. The further analyses suggest that AMO remote forcing may be a primary factor influencing interdecadal variability of PDSI in China, and interannual to interdecadal variability of PDSI seems to be closely associated with the ENSO-related events. Meanwhile, the IOBM may be a crucial factor in interannual variability of PDSI during its mature phase in the spring. In general, the SST-related dryness/wetness anomalies can be explained by the associated atmospheric circulation changes.

scholarly journals On the Interpretation of the North Atlantic Averaged Sea Surface Temperature

2020 ◽  
Vol 33 (14) ◽  
pp. 6025-6045
Author(s):  
Jing Sun ◽  
Mojib Latif ◽  
Wonsun Park ◽  
Taewook Park
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
North Atlantic ◽  
Climate Models ◽  
Ocean Dynamics ◽  
Sea Surface ◽  
The North ◽  
Sst Variability ◽  
The North Atlantic ◽  
Different Time Scales

AbstractThe North Atlantic (NA) basin-averaged sea surface temperature (NASST) is often used as an index to study climate variability in the NA sector. However, there is still some debate on what drives it. Based on observations and climate models, an analysis of the different influences on the NASST index and its low-pass filtered version, the Atlantic multidecadal oscillation (AMO) index, is provided. In particular, the relationships of the two indices with some of its mechanistic drivers including the Atlantic meridional overturning circulation (AMOC) are investigated. In observations, the NASST index accounts for significant SST variability over the tropical and subpolar NA. The NASST index is shown to lump together SST variability originating from different mechanisms operating on different time scales. The AMO index emphasizes the subpolar SST variability. In the climate models, the SST-anomaly pattern associated with the NASST index is similar. The AMO index, however, only represents pronounced SST variability over the extratropical NA, and this variability is significantly linked to the AMOC. There is a sensitivity of this linkage to the cold NA SST bias observed in many climate models. Models suffering from a large cold bias exhibit a relatively weak linkage between the AMOC and AMO and vice versa. Finally, the basin-averaged SST in its unfiltered form, which has been used to question a strong influence of ocean dynamics on NA SST variability, mixes together multiple types of variability occurring on different time scales and therefore underemphasizes the role of ocean dynamics in the multidecadal variability of NA SSTs.

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