Sub-seasonal Predictability of the Onset and Demise of the Rainy Season over Monsoonal Regions

Bombardi, Rodrigo J.; Pegion, Kathy; Kinter, James L.; Cash, Benjamin A.; Adams, Jennifer

doi:10.3389/feart.2017.00014

Cited by 43 publications

(83 citation statements)

References 51 publications

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“…Our results pertain to the entire calendar year and predictability may be higher in specific parts of the calendar year, and especially during the early and late phases of the monsoon seasons including the onset and/or demise dates. 57,58 Over the oceans, the skill is larger and the contribution of the S2S climate drivers is stronger than over land. The predictability types are organized broadly in a symmetric way, around the tropical Indian and Pacific Ocean where the skill/scale peak.…”

Section: Discussionmentioning

confidence: 99%

Tropical rainfall subseasonal-to-seasonal predictability types

Moron

Robertson

2020

npj Clim Atmos Sci

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Tropical rainfall is mostly convective and its subseasonal-to-seasonal (S2S) prediction remains challenging. We show that state-ofart model forecast skill 3 + 4 weeks ahead is systematically lower over land than ocean, which is matched by a similar land-ocean contrast in the spatial scales of observed biweekly rainfall anomalies. Regional differences in predictability are then interpreted using observed characteristics of daily rainfall (wet-patch size, mean intensity as well as the strength of local S2S modes of rainfall variation), and classified into six S2S predictability types. Both forecast skill and spatial scales are reduced over the continents, either because daily rainfall patches are small and poorly organized by S2S modes of variation (as over equatorial and northern tropical Africa), or where the daily mean intensity is very high (as over South and SE Asia). Forecast skill and spatial scales are largest where daily rainfall is synchronized by intraseasonal (such as the Madden-Julian Oscillation) as well as interannual ocean-atmosphere modes of variation (such as El Niño-Southern Oscillation), especially over northern Australia and parts of the Maritime Continent, and over parts of eastern, southern Africa and northeast South America. The oceans exhibit the highest skill and largest spatial scales, especially where interannual (central equatorial Pacific) or intraseasonal (central and eastern Tropical Indian Ocean and Western Pacific) variability is largest. These results provide a relevant regional typology of the potential drivers and controls on S2S predictability of tropical rainfall, informing intrinsic limits and possible improvements toward useful S2S climate prediction at regional scale.npj Climate and Atmospheric Science (2020) 3:4 ; https://doi.

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Section: Discussionmentioning

confidence: 99%

Tropical rainfall subseasonal-to-seasonal predictability types

Moron

Robertson

2020

npj Clim Atmos Sci

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“…Regions adjacent to the ocean display higher skill suggesting a possible role for the ocean with regards to regional predictability. Recently, Bombardi et al (2017) found that subseasonal forecasts generally outperform climatology when predicting the onset date of the monsoon, and for the AM, Moron et al (2017) found a drop in the spatial coherence of interannual variations of subseasonal to seasonal FIGURE 12 | Mean MME Week 3-4 RPSS averaged over continental monsoon regions for observed phases of Niño3.4 index (A) and MJO phases (B) measured by the RMM1 and RMM2 indices of Wheeler and Hendon (2004). Black, gray, and light gray lines correspond to the 90% level of significance over the respective North American, West African and Asian summer monsoon regions using Monte Carlo simulations.…”

Section: Discussionmentioning

confidence: 99%

Subseasonal Predictability of Boreal Summer Monsoon Rainfall from Ensemble Forecasts

Vigaud

Robertson

Tippett

et al. 2017

Front. Environ. Sci.

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Subseasonal forecast skill over the broadly defined North American (NAM), West African (WAM) and Asian (AM) summer monsoon regions is investigated using three Ensemble Prediction Systems (EPS) at sub-monthly lead times. Extended Logistic Regression (ELR) is used to produce probabilistic forecasts of weekly and week 3-4 averages of precipitation with starts in May-Aug, over the 1999-2010 period. The ELR tercile category probabilities for each model gridpoint are then averaged together with equal weight. The resulting Multi-Model Ensemble (MME) forecasts exhibit good reliability, but have generally low sharpness for forecasts beyond 1 week; Multi-model ensembling largely removes negative values of the Ranked Probability Skill Score (RPSS) seen in individual forecasts, and broadly improves the skill obtained in any of the three individual models except for the AM. The MME week 3-4 forecasts have generally higher RPSS and comparable reliability over all monsoon regions, compared to week 3 or week 4 forecast separately. Skill is higher during La Niña compared to El Niño and ENSO-neutral conditions over the 1999-2010 period, especially for the NAM. Regionally averaged RPSS is significantly correlated with the Maden-Julian Oscillation (MJO) for the AM and WAM. Our results indicate potential for skillful predictions at subseasonal time-scales over the three summer monsoon regions of the Northern Hemisphere.

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“…The prediction of monsoons and monsoon rainfall remains a challenge for state‐of‐art climate models (e.g., Becker et al ., ). However, there is evidence that timing of monsoons can be predicted on lead‐times of weeks to months (Vellinga et al ., ; Alessandri et al ., ; Bombardi et al ., ; Chevuturi et al ., ). In this section we discuss the predictability of the timing of monsoons in dynamical models, including the representation of the timing of monsoons in global climate models.…”

Section: Predictability Of Monsoon Onset and Ending Datesmentioning

confidence: 99%

“…To ensure consistent calculation of the timing of the rainy season across the globe, Bombardi et al . () start the calculation from the minimum of the first harmonic of the mean annual cycle of rainfall, while Diaconescu et al . () and Dunning et al .…”

Section: Defining Onset and End Datesmentioning

confidence: 99%

Detection, variability, and predictability of monsoon onset and withdrawal dates: A review

Bombardi

Moron

Goodnight

2019

Intl Journal of Climatology

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This article presents a review of the scientific literature on detection, sources of variability, and predictability of the timing of monsoons. The timing of monsoons is characterized by the beginning (commonly referred to as onset) and end (commonly referred to as demise, cessation, retreat, or withdrawal) dates of the summer monsoons. The main methods used to detect the timing of monsoons are divided into two categories: local‐scale methods and regional to large‐scale methods. The sources of variability of the timing of monsoons are also separated into two categories: local‐scale and large‐scale sources. Finally, the article presents a summary of the literature on the predictability of the timing of monsoons using both dynamical and statistical approaches. We show that all methods are parameterized in some way. A comparison between two different methods shows that while there might be large differences in the definition of onset and demise dates at the local level, spatial aggregation usually reduces the noise and enhances the regional monsoonal signal, which may be predictable.

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Sub-seasonal Predictability of the Onset and Demise of the Rainy Season over Monsoonal Regions

Cited by 43 publications

References 51 publications

Tropical rainfall subseasonal-to-seasonal predictability types

Tropical rainfall subseasonal-to-seasonal predictability types

Subseasonal Predictability of Boreal Summer Monsoon Rainfall from Ensemble Forecasts

Detection, variability, and predictability of monsoon onset and withdrawal dates: A review

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