The Impact of Land Surface and Atmospheric Initialization on Seasonal Forecasts with CCSM

Paolino, D. A.; Kinter, James L.; Kirtman, Ben P.; Min, Dughong; Straus, David M.

doi:10.1175/2011jcli3934.1

Cited by 34 publications

(43 citation statements)

References 39 publications

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“…It also can be speculated that the predictability source of the wet anomalies in the northern WUS in P2 is the combination of the low-frequency atmospheric internal variability and the tropical El Niño teleconnection. It has previously been reported that there is little evidence of an improved forecast of precipitation over land due to initialization of the atmosphere/land (Paolino et al 2012). However, this work shows that appropriate initialization of the atmosphere/land in addition to the ocean could substantially improve seasonal precipitation prediction.…”

Section: Conclusion and Discussionmentioning

confidence: 67%

“…Nevertheless, skillful seasonal prediction arising from the atmospheric state has been shown in some cases of summer heat waves and winter Arctic Oscillation for a one-season horizon (e.g., Jia et al 2016Jia et al , 2017. At shorter timescales, the influence of land initial conditions can be felt for weeks to months (Koster et al 2004(Koster et al , 2011Guo et al 2012;Paolino et al 2012). The intraseasonal low-frequency atmospheric oscillations with periods of 20-70 days (Ghil and Mo 1991;Plaut and Vautard 1994;Marcus et al 1994) can also modulate short-term seasonal climate prediction.…”

Section: Introductionmentioning

confidence: 99%

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On the seasonal prediction of the western United States El Niño precipitation pattern during the 2015/16 winter

et al. 2018

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A "typical" El Niño leads to wet (dry) wintertime anomalies over the southern (northern) half of the Western United States (WUS). However, during the strong El Niño of 2015/16, the WUS winter precipitation pattern was roughly opposite to this canonical (average of the record) anomaly pattern. To understand why this happened, and whether it was predictable, we use a suite of high-resolution seasonal prediction experiments with coupled climate models. We find that the unusual 2015/16 precipitation pattern was predictable at zero-lead time horizon when the ocean/atmosphere/land components were initialized with observations. However, when the ocean alone is initialized the coupled model fails to predict the 2015/16 pattern, although ocean initial conditions alone can reproduce the observed WUS precipitation during the 1997/98 strong El Niño. Further observational analysis shows that the amplitudes of the El Niño induced tropical circulation anomalies during 2015/16 were weakened by about 50% relative to those of 1997/98. This was caused by relative cold (warm) anomalies in the eastern (western) tropical Pacific suppressing (enhancing) deep convection anomalies in the eastern (western) tropical Pacific during 2015/16. The reduced El Niño teleconnection led to a weakening of the subtropical westerly jet over the southeast North Pacific and southern WUS, resulting in the unusual 2015/16 winter precipitation pattern over the WUS. This study highlights the importance of initial conditions not only in the ocean, but in the land and atmosphere as well, for predicting the unusual El Niño teleconnection and its influence on the winter WUS precipitation anomalies during 2015/16.

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Section: Conclusion and Discussionmentioning

confidence: 67%

Section: Introductionmentioning

confidence: 99%

On the seasonal prediction of the western United States El Niño precipitation pattern during the 2015/16 winter

et al. 2018

View full text Add to dashboard Cite

show abstract

“…Testing this hypothesis is one of the objectives of this study. While the impact of initialization of land and atmosphere on seasonal temperature and precipitation predictions has been previously explored in the literature (Paolino et al 2012;Koster and Suarez 2003), this study focuses instead on the impact of the increased resolution of land and atmosphere components on seasonal predictions.…”

Section: Introductionmentioning

confidence: 99%

Improved Seasonal Prediction of Temperature and Precipitation over Land in a High-Resolution GFDL Climate Model

et al. 2015

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This study demonstrates skillful seasonal prediction of 2-m air temperature and precipitation over land in a new high-resolution climate model developed by the Geophysical Fluid Dynamics Laboratory and explores the possible sources of the skill. The authors employ a statistical optimization approach to identify the most predictable components of seasonal mean temperature and precipitation over land and demonstrate the predictive skill of these components. First, the improved skill of the high-resolution model over the previous lowerresolution model in seasonal prediction of the Niño-3.4 index and other aspects of interest is shown. Then, the skill of temperature and precipitation in the high-resolution model for boreal winter and summer is measured, and the sources of the skill are diagnosed. Last, predictions are reconstructed using a few of the most predictable components to yield more skillful predictions than the raw model predictions. Over three decades of hindcasts, the two most predictable components of temperature are characterized by a component that is likely due to changes in external radiative forcing in boreal winter and summer and an ENSO-related pattern in boreal winter. The most predictable components of precipitation in both seasons are very likely ENSO-related. These components of temperature and precipitation can be predicted with significant correlation skill at least 9 months in advance. The reconstructed predictions using only the first few predictable components from the model show considerably better skill relative to observations than raw model predictions. This study shows that the use of refined statistical analysis and a high-resolution dynamical model leads to significant skill in seasonal predictions of 2-m air temperature and precipitation over land.

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“…Since the pioneering and seminal works of Shukla and Mintz (1982) and Fennessy and Shukla (1999), a number of studies have explored the potential of soil moisture for seasonal climate predictability (e.g., Koster et al 2004;Ferranti and Viterbo 2006;Douville 2010;Paolino et al 2012;Materia et al 2014). The results indicate that moisture anomalies in the soil may persist for months, determining a land surface memory, which in some areas contributes to an increase in the seasonal predictability (Douville 2010;Paolino et al 2012;Materia et al 2014).…”

Section: Issues Of Predictability Predictions and Projectionsmentioning

confidence: 99%

Coupled ocean–atmosphere modeling and predictions

Miller¹,

Collins²,

Gualdi³

et al. 2017

j mar res

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Key aspects of the current state of the ability of global and regional climate models to represent dynamical processes and precipitation variations are summarized. Interannual, decadal, and globalwarming timescales, wherein the influence of the oceans is relevant and the potential for predictability is highest, are emphasized. Oceanic influences on climate occur throughout the ocean and extend over land to affect many types of climate variations, including monsoons, the El Niño Southern Oscillation, decadal oscillations, and the response to greenhouse gas emissions. The fundamental ideas of coupling between the ocean-atmosphere-land system are explained for these modes in both global and regional contexts. Global coupled climate models are needed to represent and understand the complicated processes involved and allow us to make predictions over land and sea. Regional coupled climate models are needed to enhance our interpretation of the fine-scale response. The mechanisms by which large-scale, low-frequency variations can influence shorter timescale variations and drive regionalscale effects are also discussed. In this light of these processes, the prospects for practical climate predictability are also presented.

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The Impact of Land Surface and Atmospheric Initialization on Seasonal Forecasts with CCSM

Cited by 34 publications

References 39 publications

On the seasonal prediction of the western United States El Niño precipitation pattern during the 2015/16 winter

On the seasonal prediction of the western United States El Niño precipitation pattern during the 2015/16 winter

Improved Seasonal Prediction of Temperature and Precipitation over Land in a High-Resolution GFDL Climate Model

Coupled ocean–atmosphere modeling and predictions

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