A Gill–Matsuno‐type mechanism explains the tropical Atlantic influence on African and Indian monsoon rainfall

Kucharski, Fred; Bracco, Annalisa; Yoo, Jin; Tompkins, Adrian M.; Feudale, Laura; Ruti, Paolo; Dell’Aquila, Alessandro

doi:10.1002/qj.406

Cited by 216 publications

(189 citation statements)

References 47 publications

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“…This allows us to compare our results, obtained for a glacial base state, with similar experiments run to study the present-day monsoon variability (e.g. Kucharski et al, 2009). We then investigate if the relationship we find between tropical SST changes and those in Indian monsoon precipitation holds in additional coupled model experiments in which we force the AMOC to recover.…”

Section: Introductionmentioning

confidence: 72%

“…They show that if the ENSO (El Niño Southern Oscillation)-forced variability is subtracted, the strong Indian monsoon years correspond to cold south equatorial Atlantic sea-surface temperatures (SSTs). Kucharski et al (2009) and Losada et al (2010) also highlight the teleconnection between the tropical Atlantic and the Indian monsoon circulation at shorter timescales. By using an atmospheric general circulation model to perform sensitivity experiments to SST anomalies in the north and the south tropical Atlantic, they find the south tropical Atlantic sea-surface temperatures to be determinant in the variations of the Indian monsoon.…”

Section: Introductionmentioning

confidence: 92%

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Glacial fluctuations of the Indian monsoon and their relationship with North Atlantic climate: new data and modelling experiments

et al. 2013

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Abstract. Several paleoclimate records such as from Chinese loess, speleothems or upwelling indicators in marine sediments present large variations of the Asian monsoon system during the last glaciation. Here, we present a new record from the northern Andaman Sea (core MD77-176) which shows the variations of the hydrological cycle of the Bay of Bengal. The high-resolution record of surface water δ18O dominantly reflects salinity changes and displays large millennial-scale oscillations over the period 40 000 to 11 000 yr BP. Their timing and sequence suggests that events of high (resp. low) salinity in the Bay of Bengal, i.e. weak (resp. strong) Indian monsoon, correspond to cold (resp. warm) events in the North Atlantic and Arctic, as documented by the Greenland ice core record. We use the IPSL_CM4 Atmosphere-Ocean coupled General Circulation Model to study the processes that could explain the teleconnection between the Indian monsoon and the North Atlantic climate. We first analyse a numerical experiment in which such a rapid event in the North Atlantic is obtained under glacial conditions by increasing the freshwater flux in the North Atlantic, which results in a reduction of the intensity of the Atlantic meridional overturning circulation. This freshwater hosing results in a weakening of the Indian monsoon rainfall and circulation. The changes in the continental runoff and local hydrological cycle are responsible for an increase in salinity in the Bay of Bengal. This therefore compares favourably with the new sea water δ18O record presented here and the hypothesis of synchronous cold North Atlantic and weak Indian monsoon events. Additional sensitivity experiments are produced with the LMDZ atmospheric model to analyse the teleconnection mechanisms between the North Atlantic and the Indian monsoon. The changes over the tropical Atlantic are shown to be essential in triggering perturbations of the subtropical jet over Africa and Eurasia, that in turn affect the intensity of the Indian monsoon. These relationships are also found to be valid in additional coupled model simulations in which the Atlantic meridional overturning circulation (AMOC) is forced to resume.

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Section: Introductionmentioning

confidence: 72%

Section: Introductionmentioning

confidence: 92%

Glacial fluctuations of the Indian monsoon and their relationship with North Atlantic climate: new data and modelling experiments

et al. 2013

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“…Computationally, SPEEDY is 1 order of magnitude faster than a state-of-the-art AGCM at the same horizontal resolution, whereas the quality of the simulated climate and patterns of variability in the free atmosphere compare well with these models and with observations (Hazeleger et al, 2003;Molteni, 2003). Various studies with SPEEDY have demonstrated its ability to simulate the dominant responses to surface anomalies Bracco et al, 2007;Haarsma and Hazeleger, 2007;Kucharski et al, 2007Kucharski et al, , 2008Sterl et al, 2007;Kucharski et al, 2009). It is therefore suitable for studies on climate-vegetation feedbacks and inter-decadal or inter-centennial variability.…”

Section: Methodsmentioning

confidence: 98%

Assessing 20th century climate–vegetation feedbacks of land‐use change and natural vegetation dynamics in a fully coupled vegetation–climate model

Strengers

Müller

Schaeffer

et al. 2010

Intl Journal of Climatology

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This study describes the coupling of the dynamic global vegetation model (DGVM), Lund-Potsdam-Jena Model for managed land (LPJmL), with the general circulation model (GCM), Simplified Parameterizations primitivE Equation DYnamics model (SPEEDY), to study the feedbacks between land-use change and natural vegetation dynamics and climate during the 20th century. We show that anthropogenic land-use change had a stronger effect on climate than the natural vegetation's response to climate change (e.g. boreal greening). Changes in surface albedo are an important driver of the climate's response; but, especially in the (sub)tropics, changes in evapotranspiration and the corresponding changes in latent heat flux and cloud formation can be of equal importance in the opposite direction. Our study emphasizes that implementing dynamic vegetation into climate models is essential, especially at regional scales: the dynamic response of natural vegetation significantly alters the climate change that is driven by increased atmospheric greenhouse gas concentrations and anthropogenic land-use change.

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“…We examine these issues in the International Centre for Theoretical Physics climate model (27,28), which has been used for a number of climate problems (29,30). Here a triangular truncation at total wavenumber 30 (T30) version is used, roughly equivalent to a 3.75°× 3.75°spatial resolution.…”

Section: Climate Model Setupmentioning

confidence: 99%

Considerations for parameter optimization and sensitivity in climate models

Neelin

Bracco

Luo

et al. 2010

Proc. Natl. Acad. Sci. U.S.A.

108

120

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Climate models exhibit high sensitivity in some respects, such as for differences in predicted precipitation changes under global warming. Despite successful large-scale simulations, regional climatology features prove difficult to constrain toward observations, with challenges including high-dimensionality, computationally expensive simulations, and ambiguity in the choice of objective function. In an atmospheric General Circulation Model forced by observed sea surface temperature or coupled to a mixed-layer ocean, many climatic variables yield rms-error objective functions that vary smoothly through the feasible parameter range. This smoothness occurs despite nonlinearity strong enough to reverse the curvature of the objective function in some parameters, and to imply limitations on multimodel ensemble means as an estimator of global warming precipitation changes. Low-order polynomial fits to the model output spatial fields as a function of parameter (quadratic in model field, fourth-order in objective function) yield surprisingly successful metamodels for many quantities and facilitate a multiobjective optimization approach. Tradeoffs arise as optima for different variables occur at different parameter values, but with agreement in certain directions. Optima often occur at the limit of the feasible parameter range, identifying key parameterization aspects warranting attention-here the interaction of convection with free tropospheric water vapor. Analytic results for spatial fields of leading contributions to the optimization help to visualize tradeoffs at a regional level, e.g., how mismatches between sensitivity and error spatial fields yield regional error under minimization of global objective functions. The approach is sufficiently simple to guide parameter choices and to aid intercomparison of sensitivity properties among climate models.climate model optimization | metamodeling | precipitation bias and sensitivity I nterest in systematic parameter sensitivity and optimization has been developing both in the context of global average climate sensitivity associated with increased greenhouse gases and the effort to improve the model climatology (1-7). Some of this work has focused on variations with parameter of a climate sensitivity defined by the change of global average surface temperature under doubled CO 2 , some on optimizing the simulation of current climate features by tuning parameter values. Here we examine related questions in sensitivity and optimization, with a particular interest in precipitation. Despite capturing large-scale features, simulations of precipitation in current climate are subject to considerable regional-scale bias (8-12). A common experience is that the simulated climatology exhibits high sensitivity to parameterization changes in certain respects but nonetheless proves difficult to constrain toward observations. At the same time, predicted changes in seasonal precipitation under global warming exhibit striking disagreement among models (13-15). This manifestation of sensitivi...

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A Gill–Matsuno‐type mechanism explains the tropical Atlantic influence on African and Indian monsoon rainfall

Cited by 216 publications

References 47 publications

Glacial fluctuations of the Indian monsoon and their relationship with North Atlantic climate: new data and modelling experiments

Glacial fluctuations of the Indian monsoon and their relationship with North Atlantic climate: new data and modelling experiments

Assessing 20th century climate–vegetation feedbacks of land‐use change and natural vegetation dynamics in a fully coupled vegetation–climate model

Considerations for parameter optimization and sensitivity in climate models

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