Assessing the robustness of multidecadal variability in Northern Hemisphere wintertime seasonal forecast skill

The sensitivity to the horizontal resolution of the climate, anthropogenic climate change, and seasonal predictive skill of the ECMWF model has been studied as part of Project Athena—an international collaboration formed to test the hypothesis that substantial progress in simulating and predicting climate can be achieved if mesoscale and subsynoptic atmospheric phenomena are more realistically represented in climate models. In this study the experiments carried out with the ECMWF model (atmosphere only) are described in detail. Here, the focus is on the tropics and the Northern Hemisphere extratropics during boreal winter. The resolutions considered in Project Athena for the ECMWF model are T159 (126 km), T511 (39 km), T1279 (16 km), and T2047 (10 km). It was found that increasing horizontal resolution improves the tropical precipitation, the tropical atmospheric circulation, the frequency of occurrence of Euro-Atlantic blocking, and the representation of extratropical cyclones in large parts of the Northern Hemisphere extratropics. All of these improvements come from the increase in resolution from T159 to T511 with relatively small changes for further resolution increases to T1279 and T2047, although it should be noted that results from this very highest resolution are from a previously untested model version. Problems in simulating the Madden–Julian oscillation remain unchanged for all resolutions tested. There is some evidence that increasing horizontal resolution to T1279 leads to moderate increases in seasonal forecast skill during boreal winter in the tropics and Northern Hemisphere extratropics. Sensitivity experiments are discussed, which helps to foster a better understanding of some of the resolution dependence found for the ECMWF model in Project Athena.

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Sub-seasonal Forecast Skill for Weekly Mean Atmospheric Variability over the Northern Hemisphere in Winter and its Relationship to Mid-Latitude Teleconnections

10.1002/essoar.10503075.1 ◽

2020 ◽

Author(s):

Akio Yamagami ◽

Mio Matsueda

Keyword(s):

Northern Hemisphere ◽

Seasonal Forecast ◽

Forecast Skill ◽

Atmospheric Variability

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Variability in seasonal forecast skill of Northern Hemisphere winters over the twentieth century

Geophysical Research Letters ◽

10.1002/2017gl073736 ◽

2017 ◽

Vol 44 (11) ◽

pp. 5729-5738 ◽

Cited By ~ 25

Author(s):

Christopher H. O'Reilly ◽

James Heatley ◽

Dave MacLeod ◽

Antje Weisheimer ◽

Tim N. Palmer ◽

...

Keyword(s):

Twentieth Century ◽

Northern Hemisphere ◽

Seasonal Forecast ◽

Forecast Skill

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The northern hemisphere circumglobal teleconnection in a seasonal forecast model and its relationship to European summer forecast skill

Climate Dynamics ◽

10.1007/s00382-018-4371-4 ◽

2018 ◽

Vol 52 (5-6) ◽

pp. 3759-3771 ◽

Cited By ~ 10

Author(s):

Jonathan D. Beverley ◽

Steven J. Woolnough ◽

Laura H. Baker ◽

Stephanie J. Johnson ◽

Antje Weisheimer

Keyword(s):

Northern Hemisphere ◽

Forecast Model ◽

Seasonal Forecast ◽

Forecast Skill ◽

Circumglobal Teleconnection

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Yearly evolution of Euro-Atlantic weather regimes and of their sub-seasonal predictability

Climate Dynamics ◽

10.1007/s00382-021-05679-y ◽

2021 ◽

Author(s):

Nicola Cortesi ◽

Verónica Torralba ◽

Llorenó Lledó ◽

Andrea Manrique-Suñén ◽

Nube Gonzalez-Reviriego ◽

...

Keyword(s):

Annual Cycle ◽

Winter Season ◽

Skill Score ◽

Seasonal Forecast ◽

Forecast Skill ◽

Seasonal Forecasts ◽

Sea Level Pressure ◽

Weather Regimes ◽

Start Dates ◽

Weekly Frequency

AbstractIt is often assumed that weather regimes adequately characterize atmospheric circulation variability. However, regime classifications spanning many months and with a low number of regimes may not satisfy this assumption. The first aim of this study is to test such hypothesis for the Euro-Atlantic region. The second one is to extend the assessment of sub-seasonal forecast skill in predicting the frequencies of occurrence of the regimes beyond the winter season. Two regime classifications of four regimes each were obtained from sea level pressure anomalies clustered from October to March and from April to September respectively. Their spatial patterns were compared with those representing the annual cycle. Results highlight that the two regime classifications are able to reproduce most part of the patterns of the annual cycle, except during the transition weeks between the two periods, when patterns of the annual cycle resembling Atlantic Low regime are not also observed in any of the two classifications. Forecast skill of Atlantic Low was found to be similar to that of NAO+, the regime replacing Atlantic Low in the two classifications. Thus, although clustering yearly circulation data in two periods of 6 months each introduces a few deviations from the annual cycle of the regime patterns, it does not negatively affect sub-seasonal forecast skill. Beyond the winter season and the first ten forecast days, sub-seasonal forecasts of ECMWF are still able to achieve weekly frequency correlations of r = 0.5 for some regimes and start dates, including summer ones. ECMWF forecasts beat climatological forecasts in case of long-lasting regime events, and when measured by the fair continuous ranked probability skill score, but not when measured by the Brier skill score. Thus, more efforts have to be done yet in order to achieve minimum skill necessary to develop forecast products based on weather regimes outside winter season.

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Interannual Variations and Prediction of Spring Precipitation over China

Journal of Climate ◽

10.1175/jcli-d-17-0233.1 ◽

2018 ◽

Vol 31 (2) ◽

pp. 655-670 ◽

Cited By ~ 9

Author(s):

YuJia You ◽

Xiaojing Jia

Keyword(s):

Regression Model ◽

Yangtze River ◽

Climate Models ◽

Seasonal Forecast ◽

Forecast Skill ◽

Interannual Variations ◽

The Yangtze River ◽

Spring Precipitation ◽

Coupled Climate Models ◽

Time Variations

The interannual variations and the prediction of the leading two empirical orthogonal function (EOF) modes of spring (April–May) precipitation over China for the period from 1951 to 2014 are investigated using both observational data and the seasonal forecast made by six coupled climate models. The leading EOF mode of spring precipitation over China (EOF1-prec) features a monosign pattern, with the maximum loading located over southern China. The ENSO-related tropical Pacific SST anomalies in the previous winter can serve as a precursor for EOF1-prec. The second EOF mode of spring precipitation (EOF2-prec) over China is characterized by a dipole structure, with one pole near the Yangtze River and the other one with opposite sign over the Pearl River delta. A North Atlantic sea surface temperature (SST) anomaly dipole in the preceding March is found contribute to the prec-EOF2 and can serve as its predictor. A physics-based empirical (P-E) model is then formulated using the two precursors revealed by the observational analysis to forecast the variations of EOF1-prec and EOF2-prec. Compared to coupled climate models, which have little skill in forecasting the time variations of the two EOF modes, this P-E model can significantly improve the forecast skill of their time variations. A linear regression model is further established using the time series forecast by the P-E model to forecast the spring precipitation over China. Results suggest that the seasonal forecast skill of the spring precipitation over southeastern China, especially over the Yangtze River area, can be significantly improved by the regression model.

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Enhanced seasonal forecast skill following stratospheric sudden warmings

Nature Geoscience ◽

10.1038/ngeo1698 ◽

2013 ◽

Vol 6 (2) ◽

pp. 98-102 ◽

Cited By ~ 179

Author(s):

M. Sigmond ◽

J. F. Scinocca ◽

V. V. Kharin ◽

T. G. Shepherd

Keyword(s):

Seasonal Forecast ◽

Forecast Skill ◽

Stratospheric Sudden Warmings

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On the Level and Origin of Seasonal Forecast Skill in Northern Europe

Journal of the Atmospheric Sciences ◽

10.1175/1520-0469(1998)055<0103:otlaoo>2.0.co;2 ◽

1998 ◽

Vol 55 (1) ◽

pp. 103-127 ◽

Cited By ~ 50

Author(s):

Åke Johansson ◽

Anthony Barnston ◽

Suranjana Saha ◽

Huug van den Dool

Keyword(s):

Seasonal Forecast ◽

Forecast Skill ◽

Northern Europe

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Diagnosing Sources of U.S. Seasonal Forecast Skill

Journal of Climate ◽

10.1175/jcli3789.1 ◽

2006 ◽

Vol 19 (13) ◽

pp. 3279-3293 ◽

Cited By ~ 72

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.

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

Journal of Climate ◽

10.1175/2010jcli3527.1 ◽

2011 ◽

Vol 24 (5) ◽

pp. 1378-1395 ◽

Cited By ~ 12

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.

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