Diagnosing Sources of U.S. Seasonal Forecast Skill

2006 ◽  
Vol 19 (13) ◽  
pp. 3279-3293 ◽  
Author(s):  
X. Quan ◽  
M. Hoerling ◽  
J. Whitaker ◽  
G. Bates ◽  
T. Xu
Keyword(s):  
Air Temperature ◽  
Southern Oscillation ◽  
Surface Air Temperature ◽  
Seasonal Forecast ◽  
Forecast Skill ◽  
Diagnostic Methods ◽  
Sea Surface ◽  
Empirical Methods ◽  
Spatial Coverage ◽  
Sst Anomalies

Abstract In this study the authors diagnose the sources for the contiguous U.S. seasonal forecast skill that are related to sea surface temperature (SST) variations using a combination of dynamical and empirical methods. The dynamical methods include ensemble simulations with four atmospheric general circulation models (AGCMs) forced by observed monthly global SSTs from 1950 to 1999, and ensemble AGCM experiments forced by idealized SST anomalies. The empirical methods involve a suite of reductions of the AGCM simulations. These include uni- and multivariate regression models that encapsulate the simultaneous and one-season lag linear connections between seasonal mean tropical SST anomalies and U.S. precipitation and surface air temperature. Nearly all of the AGCM skill in U.S. precipitation and surface air temperature, arising from global SST influences, can be explained by a single degree of freedom in the tropical SST field—that associated with the linear atmospheric signal of El Niño–Southern Oscillation (ENSO). The results support previous findings regarding the preeminence of ENSO as a U.S. skill source. The diagnostic methods used here exposed another skill source that appeared to be of non-ENSO origins. In late autumn, when the AGCM simulation skill of U.S. temperatures peaked in absolute value and in spatial coverage, the majority of that originated from SST variability in the subtropical west Pacific Ocean and the South China Sea. Hindcast experiments were performed for 1950–99 that revealed most of the simulation skill of the U.S. seasonal climate to be recoverable at one-season lag. The skill attributable to the AGCMs was shown to achieve parity with that attributable to empirical models derived purely from observational data. The diagnostics promote the interpretation that only limited advances in U.S. seasonal prediction skill should be expected from methods seeking to capitalize on sea surface predictors alone, and that advances that may occur in future decades could be readily masked by inherent multidecadal fluctuations in skill of coupled ocean–atmosphere systems.

Interdecadal Changes in the Relationship between Wintertime Surface Air Temperature over the Indo-China Peninsula and ENSO

2021 ◽  
pp. 1-45
Author(s):  
Juncong Li ◽  
Zhiping Wen ◽  
Xiuzhen Li ◽  
Yuanyuan Guo
Keyword(s):  
Air Temperature ◽  
Southern Oscillation ◽  
Surface Air Temperature ◽  
Seasonal Prediction ◽  
Atmospheric Model ◽  
Sea Surface ◽  
Warm Temperature ◽  
Southwesterly Wind ◽  
The Relationship ◽  
Sst Anomalies

AbstractInterdecadal variations of the relationship between El Niño-Southern Oscillation (ENSO) and the Indo-China Peninsula (ICP) surface air temperature (SAT) in winter are investigated in the study. Generally, there exists a positive correlation between them during 1958–2015 because the ENSO-induced anomalous western North Pacific anticyclone (WNPAC) is conducive to pronounced temperature advection anomalies over the ICP. However, such correlation is unstable in time, having experienced a high-to-low transition around the mid-1970s and a recovery since the early-1990s. This oscillating relationship is owing to the anomalous WNPAC intensity in different decades. During the epoch of high correlation, the anomalous WNPAC and associated southwesterly winds over the ICP are stronger, which brings amounts of warm temperature advections and markedly heats the ICP. Differently, a weaker WNPAC anomaly and insignificant ICP SAT anomalies are the circumstances for the epoch of low correlation. It is also found that substantial southwesterly wind anomalies over the ICP related to the anomalous WNPAC occur only when large sea surface temperature (SST) anomalies over the northwest Indian Ocean (NWIO) coincide with ENSO (namely when the ENSO-NWIO SST connection is strong). The NWIO SST anomalies are capable of driving favorable atmospheric circulation that effectively alters ICP SAT and efficiently modulates the ENSO-ICP SAT correlation, which is further supported by numerical simulations utilizing the Community Atmospheric Model, version 4 (CAM4). This paper emphasizes the non-stationarity of the ENSO-ICP SAT relationship and also uncovers the underlying modulation factors, which has important implications for the seasonal prediction of the ICP temperature.

Forecasting North America Winter Surface Air Temperature Using Machine Learning Methods

2020 ◽  
Author(s):  
Qifeng Qian ◽  
Xiaojing Jia ◽  
Hai Lin
Keyword(s):  
Machine Learning ◽  
North America ◽  
Air Temperature ◽  
Climate Models ◽  
Surface Air Temperature ◽  
Seasonal Forecast ◽  
Forecast Skill ◽  
Gradient Boosting ◽  
Support Vector ◽  
Extreme Gradient Boosting

<p>Two machine learning (ML) models (Support Vector Regression and Extreme Gradient Boosting; SVR and XGBoost hereafter) have been developed to perform seasonal forecast for the winter (December–January–February, DJF) surface air temperature (SAT) in North America (NA) in this study. The seasonal forecast skills of the two ML models are evaluated in a cross-validated fashion. Forecast results from one Linear Regression (LR and hereafter) model and two Canadian dynamic climate models are used for the purpose of a comparison. In the take-one-out hindcast experiment, the two ML models and the LR model show reasonable seasonal forecast skills for the winter SAT in NA. Comparing to the two Canadian dynamic models, the two ML models and the LR model have better forecast skill for the winter SAT over the central NA which mainly get contribution of a skillful forecast of the second Empirical Orthogonal Function (EOF) mode of winter SAT over NA. In general, the SVR model and XGBoost model hindcasts show better forecast performances than LR model. However, the LR model shows less dependence on the size of the training dataset than SVR and XGBoost models. In the real forecast experiments during the period 2011-2017, compared to the two Canadian dynamic climate models, the two ML models clearly improve the forecast skill of winter SAT over northern and central NA. The results of this study suggest that ML models may provide real-time supplementary forecast tools to improve the forecast skill and may operationally facilitate the seasonal forecast of the winter climate of NA. </p>

scholarly journals Origins and Levels of Seasonal Forecast Skill for Sea Ice in Hudson Bay Using Canonical Correlation Analysis

2011 ◽  
Vol 24 (5) ◽  
pp. 1378-1395 ◽  
Author(s):  
Adrienne Tivy ◽  
Stephen E. L. Howell ◽  
Bea Alt ◽  
John J. Yackel ◽  
Thomas Carrieres
Keyword(s):  
Correlation Analysis ◽  
Sea Ice ◽  
Canonical Correlation Analysis ◽  
North Atlantic ◽  
Canonical Correlation ◽  
Seasonal Forecast ◽  
Forecast Skill ◽  
Sea Surface ◽  
Hudson Bay ◽  
Ice Concentration

Abstract Canonical correlation analysis (CCA) is used to estimate the levels and sources of seasonal forecast skill for July ice concentration in Hudson Bay over the 1971–2005 period. July is an important transition month in the seasonal cycle of sea ice in Hudson Bay because it is the month when the sea ice clears enough to allow the first passage of ships to the Port of Churchill. Sea surface temperature (quasi global, North Atlantic, and North Pacific), Northern Hemisphere 500-mb geopotential height (z500), sea level pressure (SLP), and regional surface air temperature (SAT) are tested as predictors at 3-, 6-, and 9-month lead times. The model with the highest skill has three predictors—fall North Atlantic SST, fall z500, and fall SAT—and significant tercile forecast skill covering 61% of the Hudson Bay region. The highest skill for a single-predictor model is from fall North Atlantic SST (6-month lead). Fall SST explains 69% of the variance in July ice concentration in Hudson Bay and a possible atmospheric link that accounts for the lagged relationship is presented. CCA diagnostics suggest that changes in the subpolar North Atlantic gyre and the Atlantic multidecadal oscillation (AMO), reflected in sea surface temperature, precedes a deepening/weakening of the winter upper-air ridge northwest of Hudson Bay. Changes in the height of the ridge are reflected in the strength of the winter northwesterly winds over Hudson Bay that have a direct impact on the winter ice thickness distribution; anomalies in winter ice severity are later reflected in the pattern and timing of spring breakup. July ice concentration in Hudson Bay has declined by approximately 20% per decade between 1979 and 2007, and the hypothesized link to the AMO may help explain this significant loss of ice.

scholarly journals Interannual variability of the Arabian Sea warm pool intensity and its association with monsoon onset over Kerala

MAUSAM ◽  
2021 ◽  
Vol 58 (3) ◽  
pp. 345-350
Author(s):  
O. P. SINGH
Keyword(s):  
Arabian Sea ◽  
Southern Oscillation Index ◽  
Southern Oscillation ◽  
Warm Pool ◽  
Monsoon Onset ◽  
Sea Surface ◽  
Onset Date ◽  
Peak Intensity ◽  
Sst Anomalies ◽  
Lakshadweep Sea

In this paper the relationships between the Arabian Sea warm pool intensity, Southern Oscillation (SO) and the monsoon onset have been discussed. The results show that the peak intensity of the warm pool in the Lakshadweep Sea is significantly correlated with the monsoon onset date over Kerala. Warmer Sea Surface Temperature (SST) anomalies in the warm pool region during April-May are associated with delayed monsoon onset over Kerala though the cause-and-effect relationship is not known. The Southern Oscillation Index (SOI) of March can provide predictive indications of the peak intensity of the warm pool which, normally occurs during April.

scholarly journals Interannual Variability of Summer Surface Air Temperature over Central India: Implications for Monsoon Onset

2019 ◽  
Vol 32 (6) ◽  
pp. 1693-1706 ◽  
Author(s):  
Zhen-Qiang Zhou ◽  
Renhe Zhang ◽  
Shang-Ping Xie
Keyword(s):  
Indian Ocean ◽  
Air Temperature ◽  
Summer Monsoon ◽  
El Niño ◽  
El Nino ◽  
Surface Air Temperature ◽  
Central India ◽  
Monsoon Onset ◽  
Summer Monsoon Onset ◽  
Sst Anomalies

Abstract Year-to-year variability of surface air temperature (SAT) over central India is most pronounced in June. Climatologically over central India, SAT peaks in May, and the transition from the hot premonsoon to the cooler monsoon period takes place around 9 June, associated with the northeastward propagation of intraseasonal convective anomalies from the western equatorial Indian Ocean. Positive (negative) SAT anomalies during June correspond to a delayed (early) Indian summer monsoon onset and tend to occur during post–El Niño summers. On the interannual time scale, positive SAT anomalies of June over central India are associated with positive SST anomalies over both the equatorial eastern–central Pacific and Indian Oceans, representing El Niño effects in developing and decay years, respectively. Although El Niño peaks in winter, the correlations between winter El Niño and Indian SAT peak in the subsequent June, representing a post–El Niño summer capacitor effect associated with positive SST anomalies over the north Indian Ocean. These results have important implications for the prediction of Indian summer climate including both SAT and summer monsoon onset over central India.

scholarly journals Seasonal Variation and Spatial Patterns of the Atmospheric Component of the Pacific Decadal Oscillation

2013 ◽  
Vol 26 (5) ◽  
pp. 1575-1594 ◽  
Author(s):  
Catrin M. Mills ◽  
John E. Walsh
Keyword(s):  
United States ◽  
North America ◽  
Air Temperature ◽  
Pacific Decadal Oscillation ◽  
Southern Oscillation ◽  
Grid Point ◽  
Southeastern United States ◽  
Surface Air Temperature ◽  
The Pacific ◽  
Difference Fields

Abstract The Pacific decadal oscillation (PDO) is an El Niño–Southern Oscillation (ENSO)-like climate oscillation that varies on multidecadal and higher-frequency scales, with a sea surface temperature (SST) dipole in the Pacific. This study addresses the seasonality, vertical structure, and across-variable relationships of the local North Pacific and downstream North American atmospheric signal of the PDO. The PDO-based composite difference fields of 500-mb geopotential height, surface air temperature, sea level pressure, and precipitation vary not only across seasons, but also from one calendar month to another within a season, although month-to-month continuity is apparent. The most significant signals occur in western North America and in the southeastern United States, where a positive PDO is associated with negative heights, consistent with underlying temperatures in the winter. In summer, a negative precipitation signal in the southeastern United States associated with a positive PDO phase is consistent with a ridge over the region. When an annual harmonic is fit to the 12 monthly surface air temperature differences at each grid point, the PDO temperature signal peaks in winter in most of North America, while a peak in summer occurs in the southeastern United States. Approximately 25% of the variance of the PDO index is accounted for by ENSO. Atmospheric composite differences based on a residual (ENSO linearly removed) PDO index have many similarities to those of the full PDO signal.

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