We analyse the ability of CMIP3 and CMIP5 coupled ocean–atmosphere general circulation models (CGCMs) to simulate the tropical Pacific mean state and El Niño-Southern Oscillation (ENSO). The CMIP5 ...multi-model ensemble displays an encouraging 30 % reduction of the pervasive cold bias in the western Pacific, but no quantum leap in ENSO performance compared to CMIP3. CMIP3 and CMIP5 can thus be considered as one large ensemble (CMIP3 + CMIP5) for multi-model ENSO analysis. The too large diversity in CMIP3 ENSO amplitude is however reduced by a factor of two in CMIP5 and the ENSO life cycle (location of surface temperature anomalies, seasonal phase locking) is modestly improved. Other fundamental ENSO characteristics such as central Pacific precipitation anomalies however remain poorly represented. The sea surface temperature (SST)-latent heat flux feedback is slightly improved in the CMIP5 ensemble but the wind-SST feedback is still underestimated by 20–50 % and the shortwave-SST feedbacks remain underestimated by a factor of two. The improvement in ENSO amplitudes might therefore result from error compensations. The ability of CMIP models to simulate the SST-shortwave feedback, a major source of erroneous ENSO in CGCMs, is further detailed. In observations, this feedback is strongly nonlinear because the real atmosphere switches from subsident (positive feedback) to convective (negative feedback) regimes under the effect of seasonal and interannual variations. Only one-third of CMIP3 + CMIP5 models reproduce this regime shift, with the other models remaining locked in one of the two regimes. The modelled shortwave feedback nonlinearity increases with ENSO amplitude and the amplitude of this feedback in the spring strongly relates with the models ability to simulate ENSO phase locking. In a final stage, a subset of metrics is proposed in order to synthesize the ability of each CMIP3 and CMIP5 models to simulate ENSO main characteristics and key atmospheric feedbacks.
Synoptic wind events in the equatorial Pacific strongly influence the El Niño/Southern Oscillation (ENSO) evolution. This paper characterizes the spatio-temporal distribution of Easterly (EWEs) and ...Westerly Wind Events (WWEs) and quantifies their relationship with intraseasonal and interannual large-scale climate variability. We unambiguously demonstrate that the Madden–Julian Oscillation (MJO) and Convectively-coupled Rossby Waves (CRW) modulate both WWEs and EWEs occurrence probability. 86 % of WWEs occur within convective MJO and/or CRW phases and 83 % of EWEs occur within the suppressed phase of MJO and/or CRW. 41 % of WWEs and 26 % of EWEs are in particular associated with the combined occurrence of a CRW/MJO, far more than what would be expected from a random distribution (3 %). Wind events embedded within MJO phases also have a stronger impact on the ocean, due to a tendency to have a larger amplitude, zonal extent and longer duration. These findings are robust irrespective of the wind events and MJO/CRW detection methods. While WWEs and EWEs behave rather symmetrically with respect to MJO/CRW activity, the impact of ENSO on wind events is asymmetrical. The WWEs occurrence probability indeed increases when the warm pool is displaced eastward during El Niño events, an increase that can partly be related to interannual modulation of the MJO/CRW activity in the western Pacific. On the other hand, the EWEs modulation by ENSO is less robust, and strongly depends on the wind event detection method. The consequences of these results for ENSO predictability are discussed.
In this paper, we evaluate several timely, daily air-sea heat flux products (NCEP, NCEP2, ERA-Interim and OAFlux/ISCCP) against observations and present the newly developed TropFlux product. This new ...product uses bias-corrected ERA-interim and ISCCP data as input parameters to compute air-sea fluxes from the COARE v3.0 algorithm. Wind speed is corrected for mesoscale gustiness. Surface net shortwave radiation is based on corrected ISCCP data. We extend the shortwave radiation time series by using “near real-time” SWR estimated from outgoing longwave radiation. All products reproduce consistent intraseasonal surface net heat flux variations associated with the Madden-Julian Oscillation in the Indian Ocean, but display more disparate interannual heat flux variations associated with El Niño in the eastern Pacific. They also exhibit marked differences in mean values and seasonal cycle. Comparison with global tropical moored buoy array data, I-COADS and fully independent mooring data sets shows that the two NCEP products display lowest correlation to mooring turbulent fluxes and significant biases. ERA-interim data captures well temporal variability, but with significant biases. OAFlux and TropFlux perform best. All products have issues in reproducing observed longwave radiation. Shortwave flux is much better captured by ISCCP data than by any of the re-analyses. Our “near real-time” shortwave radiation performs better than most re-analyses, but tends to underestimate variability over the cold tongues of the Atlantic and Pacific. Compared to independent mooring data, NCEP and NCEP2 net heat fluxes display ~0.78 correlation and >65 W m
−2
rms-difference, ERA-I performs better (~0.86 correlation and ~48 W m
−2
) while OAFlux and TropFlux perform best (~0.9 correlation and ~43 W m
−2
). TropFlux hence provides a useful option for studying flux variability associated with ocean–atmosphere interactions, oceanic heat budgets and climate fluctuations in the tropics.
Salinity observing satellites have the potential to monitor river fresh-water plumes mesoscale spatio-temporal variations better than any other observing system. In the case of the Soil Moisture and ...Ocean Salinity (SMOS) satellite mission, this capacity was hampered due to the contamination of SMOS data processing by strong land-sea emissivity contrasts. Kolodziejczyk et al. (2016) (hereafter K2016) developed a methodology to mitigate SMOS systematic errors in the vicinity of continents, that greatly improved the quality of the SMOS Sea Surface Salinity (SSS). Here, we find that SSS variability, however, often remained underestimated, such as near major river mouths. We revise the K2016 methodology with: a) a less stringent filtering of measurements in regions with high SSS natural variability (inferred from SMOS measurements) and b) a correction for seasonally-varying latitudinal systematic errors. With this new mitigation, SMOS SSS becomes more consistent with the independent SMAP SSS close to land, for instance capturing consistent spatio-temporal variations of low salinity waters in the Bay of Bengal and Gulf of Mexico. The standard deviation of the differences between SMOS and SMAP weekly SSS is <0.3 pss in most of the open ocean. The standard deviation of the differences between 18-day SMOS SSS and 100-km averaged ship SSS is 0.20 pss (0.24 pss before correction) in the open ocean. Even if this standard deviation of the differences increases closer to land, the larger SSS variability yields a more favorable signal-to-noise ratio, with r2 between SMOS and SMAP SSS larger than 0.8. The correction also reduces systematic biases associated with man-made Radio Frequency Interferences (RFI), although SMOS SSS remains more impacted by RFI than SMAP SSS. This newly-processed dataset will allow the analysis of SSS variability over a larger than 8 years period in regions previously heavily influenced by land-sea contamination, such as the Bay of Bengal or the Gulf of Mexico.
•Improved SMOS salinity systematic error correction from Kolodziejczyk et al. (2016)•Refined variability of sea surface salinity near e.g. major river mouths•Consistent mesoscale patterns observed by SMOS and SMAP satellite missions
Extreme El Niño events have outsized impacts and strongly contribute to the El Niño Southern Oscillation (ENSO) warm/cold phase asymmetries. There is currently no consensus on the respective ...importance of oceanic and atmospheric nonlinearities for those asymmetries. Here, we use atmospheric and oceanic general circulation models that reproduce ENSO asymmetries well to quantify the atmospheric nonlinearities contribution. The linear and nonlinear components of the wind stress response to Sea Surface Temperature (SST) anomalies are isolated using ensemble atmospheric experiments, and used to force oceanic experiments. The wind stress-SST nonlinearity is dominated by the deep atmospheric convective response to SST. This wind-stress nonlinearity contributes to ~ 40% of the peak amplitude of extreme El Niño events and ~ 55% of the prolonged eastern Pacific warming they generate until the following summer. This large contribution arises because nonlinearities consistently drive equatorial westerly anomalies, while the larger linear component is made less efficient by easterly anomalies in the western Pacific during fall and winter. Overall, wind-stress nonlinearities fully account for the eastern Pacific positive ENSO skewness. Our findings underscore the pivotal role of atmospheric nonlinearities in shaping extreme El Niño events and, more generally, ENSO asymmetry.
The densely populated Bay of Bengal (BoB) rim witnesses the deadliest tropical cyclones (TCs) globally, before and after the summer monsoon. Previous studies indicated that enhanced salinity and ...reduced thermal stratification reduce cooling under BoB TCs after the monsoon, suggesting that air‐sea coupling may favor stronger TCs during that season. Using observations and simulations from a one fourth degree regional ocean‐atmosphere model, we show that BoB TCs are stronger before the monsoon due to a more favorable large‐scale background state (less vertical wind shear and higher sea surface temperature). Air‐sea coupling however alleviates this background state influence, by reducing the number of premonsoon intense TCs, because of larger cooling and reduced upward enthalpy fluxes below TCs during that season. As the impact of air‐sea interactions on BoB TCs is largest for intense TCs, it should be further investigated for Category 3 and above TCs, which are not reproduced at one fourth degree resolution.
Plain language summary
Tropical cyclones that develop in the Bay of Bengal are amongst the most lethal globally, owing to the dense and vulnerable coastal population living along its rim. These cyclones mostly occur in April–May and October–December, that is, before and after the Indian summer monsoon. In this study, we show that there are more intense cyclones before than after the monsoon, because of more favorable large‐scale background conditions. We however also show that more intense interactions between the cyclone and the ocean tend to reduce risks for intense cyclones before the monsoon, hence opposing the effect of background conditions. Since air‐sea coupling clearly contributes to the Bay of Bengal cyclone intensity, in particular before the monsoon, it should be accounted for in operational forecasts.
Key Points
Bay of Bengal tropical cyclones are stronger before than after the monsoon, due to more favorable large‐scale background conditions
Air‐sea coupling alleviates the effect of large‐scale background conditions, by inhibiting premonsoon tropical cyclones
The air‐sea coupling negative feedback on tropical cyclones is weak after the monsoon, due to a fresher, less thermally stratified BoB
Monsoon rain and rivers bring large freshwater input to the Northern Bay of Bengal (BoB), yielding low Sea Surface Salinity (SSS) after the monsoon. The resulting sharp upper-ocean salinity ...stratification is thought to influence tropical cyclones intensity and biological productivity by inhibiting vertical mixing. Despite recent progresses, the density of in situ data is far from sufficient to monitor the BoB SSS variability, even at the seasonal timescale. The advent of satellite remotely-sensed SSS (SMOS, Aquarius, SMAP) offers a unique opportunity to provide synoptic maps of the BoB SSS every ~8 days. Previous SMOS SSS retrievals did not perform well in the BoB. Here, we show that improved systematic error corrections and quality control procedures yield a much better performance of the new “debiased v4” CATDS level-3 SSS from SMOS (~0.8 correlation, 0.04 bias and 0.64 root-mean-square difference to more than 28,000 collocated in situ data points over 2010–2019). The SMOS product now performs equivalently to Aquarius, and is slightly inferior to SMAP over the BoB. In particular, SMAP and SMOS are able to capture salinity variations close to the east coast of India (r > 0.8 within 75–150 km of the coast). They thus capture the seasonal freshening there, associated with equatorward advection of the Northern BoB low-salinity water by the East Indian Coastal Current (EICC) after the summer monsoon. The 10-year long SMOS record further allows to describe the BoB interannual SSS variability, which is strongest in boreal fall in relation with the Indian Ocean Dipole (IOD). Positive IOD events induce a weakening of the southward export of freshwater by the EICC, and hence negative SSS anomalies in the Northern BoB and positive ones along the East Indian coast. This confirms results from earlier studies based on modelling, sparse in situ data, or shorter satellite records, but this time from a 10-year long SSS record. Overall, our study indicates that the new SMOS retrieval can be confidently used to monitor the BoB SSS and to study its mechanisms. We end by a brief description of the BoB SSS anomalies associated with the extreme 2019 IOD event and highlight the very good performance over the BoB of a new multi-satellite product developed by the European Space Agency merging SMOS, Aquarius and SMAP data.
•The new debiased CATDS SMOS SSS product resolves major issues in the Bay of Bengal.•New SMOS has a comparable quality with SMAP and Aquarius, but over a full decade.•Confirms the post-monsoon southward transport of low saline water by the EICC.•Confirms that this transport is interannually modulated by the Indian Ocean Dipole.
Interannual sea level anomalies (SLA), and the related thermocline variations, along the west coast of India (WCI) strongly impact the ecosystems, fisheries, and potentially the monsoon rainfall. ...Here we investigate the mechanisms driving the WCI interannual SLA using a linear continuously stratified ocean model, which realistically simulates the leading northern Indian Ocean SLA mode associated with the Indian Ocean Dipole (IOD). During, for example, positive IOD events, easterly wind anomalies near Sri Lanka in late summer and fall force downwelling coastal Kelvin waves, which induce positive WCI SLA within days. Meanwhile, equatorial easterlies force upwelling Kelvin waves that travel to WCI through the Bay of Bengal coastal waveguide. Part of this opposite signal also transits slowly through the Bay of Bengal interior as Rossby waves, eventually yielding negative SLA along the WCI in winter. The WCI SLA thus shifts from positive in fall to negative in winter during positive IOD events.
Plain Language Summary
The Indian Ocean Dipole (IOD) is the leading mode of Indian Ocean climate variability and is associated with anomalous winds over the equatorial Indian Ocean. A recent work has demonstrated that IOD events could strengthen or inhibit the upwelling of poorly oxygenated waters along the west coast of India (WCI). Such an upwelling impacts oxygen distribution in the upper layers and can thus have adverse effects on ecosystem and fisheries. Here we explain the mechanisms linking the IOD to upwelling along the WCI. IOD zonal winds in the central equatorial Indian Ocean produce two opposite signals on WCI. One that travels fast, directly up the WCI during summer and fall. But another opposite‐polarity signal follows equator and Bay of Bengal rim—a part of which transits slowly through the Bay of Bengal interior, eventually reaching the WCI and flipping the sign of sea level anomalies there in winter.
Key Points
Positive Indian Ocean Dipoles induce positive (negative) sea level anomalies in boreal fall (winter) along the west coast of India
Fall easterly wind anomalies near Sri Lanka force downwelling coastal Kelvin waves that travel quickly to the west coast of India
Fall eastern equatorial upwelling signals travel more slowly through the interior Bay of Bengal, reaching the Indian west coast in winter
RAMA McPhaden, M. J.; Meyers, G.; Ando, K. ...
Bulletin of the American Meteorological Society,
04/2009, Letnik:
90, Številka:
4
Journal Article
Recenzirano
Odprti dostop
The Indian Ocean is unique among the three tropical ocean basins in that it is blocked at 25°N by the Asian landmass. Seasonal heating and cooling of the land sets the stage for dramatic monsoon wind ...reversals, strong ocean–atmosphere interactions, and intense seasonal rains over the Indian subcontinent, Southeast Asia, East Africa, and Australia. Recurrence of these monsoon rains is critical to agricultural production that supports a third of the world's population. The Indian Ocean also remotely influences the evolution of El Niño–Southern Oscillation (ENSO), the North Atlantic Oscillation (NAO), North American weather, and hurricane activity. Despite its importance in the regional and global climate system though, the Indian Ocean is the most poorly observed and least well understood of the three tropical oceans.
This article describes the Research Moored Array for African–Asian–Australian Monsoon Analysis and Prediction (RAMA), a new observational network designed to address outstanding scientific questions related to Indian Ocean variability and the monsoons. RAMA is a multinationally supported element of the Indian Ocean Observing System (IndOOS), a combination of complementary satellite and in situ measurement platforms for climate research and forecasting. The article discusses the scientific rationale, design criteria, and implementation of the array. Initial RAMA data are presented to illustrate how they contribute to improved documentation and understanding of phenomena in the region. Applications of the data for societal benefit are also described.
Previous studies suggest that the winter surface freshening in the southeastern Arabian Sea (SEAS) contributes to the development of very high Sea Surface Temperatures (SST) thereby influencing the ...following summer monsoon onset. Here, we use forced and coupled simulations with a regional ocean general circulation model to explore the SEAS Sea Surface Salinity (SSS) variability mechanisms and impact on the monsoon. Both configurations capture the main SEAS oceanographic features, and confirm that the winter SSS decrease results from horizontal advection of Bay of Bengal freshwater by the cyclonic circulation around India during fall. A coupled model sensitivity experiment where salinity has no effect on mixing indicates that the salinity stratification reduces the SEAS mixed layer cooling by vertical processes by 3 °C seasonally. Salinity however enhances mixed layer cooling by a similar amount through concentrating negative winter surface heat fluxes into a thinner mixed layer, resulting in no climatological impact on SST and summer monsoon rainfall. The Indian Ocean Dipole (IOD) is the main driver of the winter SEAS SSS interannual variability (r ~ 0.8). Salty anomalies generated in the western Bay of Bengal during fall by positive IOD events are indeed transported by the cyclonic climatological coastal circulation, reaching the SEAS in winter. By this time, warm IOD-induced SST anomalies in the SEAS are already decaying, and the SEAS SSS anomalies hence do not contribute to their development. Overall, our model results suggest a weak climatological and interannual impact of the SEAS winter freshening on local SST and following monsoon onset.
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