Tropical deep convection exhibits complex organization over a wide range of scales. This study investigates the relationships between the spatial organization of deep convection and the large-scale atmospheric state. By using several satellite datasets and reanalyses, and by defining a simple diagnostic of convective aggregation, relationships between the degree of convective aggregation and the amount of water vapor, turbulent surface fluxes, and radiation are highlighted above tropical oceans. When deep convection is more aggregated, the middle and upper troposphere are drier in the convection-free environment, turbulent surface fluxes are enhanced, and the low-level and midlevel cloudiness is reduced in the environment. Humidity and cloudiness changes lead to a large increase in outgoing longwave radiation. Cloud changes also result in reduced reflected shortwave radiation. Owing to these opposing effects, the sensitivity of the radiative budget at the top of the atmosphere to convective aggregation turns out to be weak, but the distribution of radiative heating throughout the troposphere is affected. These results suggest that feedbacks between convective aggregation and the large-scale atmospheric state might influence large-scale dynamics and the transports of water and energy and, thus, play a role in the climate variability and change. These observational findings are qualitatively consistent with previous cloud-resolving model results, except for the effects on cloudiness and reflected shortwave radiation. The proposed methodology may be useful for assessing the representation of convective aggregation and its interaction with the large-scale atmospheric state in various numerical models.
[1] Recent progress is reviewed in the understanding of convective interaction with water vapor and changes associated with water vapor in warmer climates. Progress includes new observing techniques (including isotopic methods) that are helping to illuminate moisture-convection interaction, better observed humidity trends, new modeling approaches, and clearer expectations as to the hydrological consequences of increased specific humidity in a warmer climate. A theory appears to be in place to predict humidity in the free troposphere if winds are known at large scales, providing a crucial link between small-scale behavior and large-scale mass and energy constraints. This, along with observations, supports the anticipated water vapor feedback on climate, though key uncertainties remain connected to atmospheric dynamics and the hydrological consequences of a moister atmosphere. More work is called for to understand how circulations on all scales are governed and what role water vapor plays. Suggestions are given for future research.
Abstract. The AMMA (African Monsoon Multidisciplinary Analysis) program is dedicated to providing a better understanding of the West African monsoon and its influence on the physical, chemical and biological environment regionally and globally, as well as relating variability of this monsoon system to issues of health, water resources, food security and demography for West African nations. Within this framework, an intensive field campaign took place during the summer of 2006 to better document specific processes and weather systems at various key stages of this monsoon season. This campaign was embedded within a longer observation period that documented the annual cycle of surface and atmospheric conditions between 2005 and 2007. The present paper provides a large and regional scale overview of the 2006 summer monsoon season, that includes consideration of of the convective activity, mean atmospheric circuCorrespondence to: S. Janicot (serge.janicot@locean-ipsl.upmc.fr) lation and synoptic/intraseasonal weather systems, oceanic and land surface conditions, continental hydrology, dust concentration and ozone distribution. The 2006 African summer monsoon was a near-normal rainy season except for a large-scale rainfall excess north of 15 • N. This monsoon season was also characterized by a 10-day delayed onset compared to climatology, with convection becoming developed only after 10 July. This onset delay impacted the continental hydrology, soil moisture and vegetation dynamics as well as dust emission. More details of some less-well-known atmospheric features in the African monsoon at intraseasonal and synoptic scales are provided in order to promote future research in these areas.
[1] Tropical deep convection exhibits a variety of levels of aggregation over a wide range of scales. Based on a multisatellite analysis, the present study shows at mesoscale that different levels of aggregation are statistically associated with differing large-scale atmospheric states, despite similar convective intensity and large-scale forcings. The more aggregated the convection, the dryer and less cloudy the atmosphere, the stronger the outgoing longwave radiation, and the lower the planetary albedo. This suggests that mesoscale convective aggregation has the potential to affect couplings between moisture and convection and between convection, radiation, and large-scale ascent. In so doing, aggregation may play a role in phenomena such as ''hot spots'' or the Madden-Julian Oscillation. These findings support the need for the representation of mesoscale organization in cumulus parameterizations; most parameterizations used in current climate models lack any such representation. The ability of a cloud systemresolving model to reproduce observed relationships suggests that such models may be useful to guide attempts at parameterizations of convective aggregation.
Meyssignac et al. Measuring OHC to Estimate the EEI efficient approach to estimate EEI. In this community paper we review the current four state-of-the-art methods to estimate global OHC changes and evaluate their relevance to derive EEI estimates on different time scales. These four methods make use of: (1) direct observations of in situ temperature; (2) satellite-based measurements of the ocean surface net heat fluxes; (3) satellite-based estimates of the thermal expansion of the ocean and (4) ocean reanalyses that assimilate observations from both satellite and in situ instruments. For each method we review the potential and the uncertainty of the method to estimate global OHC changes. We also analyze gaps in the current capability of each method and identify ways of progress for the future to fulfill the requirements of EEI monitoring. Achieving the observation of EEI with sufficient accuracy will depend on merging the remote sensing techniques with in situ measurements of key variables as an integral part of the Ocean Observing System.
Abstract. The interplay between large scale dynamics and tropospheric moisture is investigated. A simple conceptual model of the sources and sinks of humidity is used to reconstruct, using a backward Lagrangian trajectory technique, the water vapor distribution in the tropical and subtropical free troposphere. Satellite data in the water vapor channel from both Meteosat-3 and Meteosat-4 satellites are then used to validate the model following a model-to-satellite approach over the whole Atlantic ocean. There is excellent agreement between simulations and observations in the drier regions, but the simulated brightness temperature exhibits a warm bias within and near moist, convective regions. This bias is most probably due to the neglect of cloud effects in reconstructing the simulated brightness temperature, rather than to a dry bias in the simulation. A second advective simulation, performed with monthly mean rather than full transient winds, led to a substantially drier subtropics. This calculation demonstrates the importance of synoptic scale transient eddies in determining the humidity of the subtropical dry zones. It is speculated on this basis that discontinuous changes in synoptic eddy activity could provide a mechanism for rapid global climate changes..
In situ and satellite observations reveal that the tropical intraseasonal oscillation is occasionally associated with large variations in sea surface temperature (SST). The purpose of this paper is to find the physical origin of such strong SST perturbations (up to 3 K) over the Indian Ocean by examining two intraseasonal events in January and March 1999. Analysis of SST data from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and from drifting buoys reveals that these two intraseasonal events deeply modify the SST field between the equator and 10ЊS, while the surface flux perturbation extends over a wide area of the tropical Indian Ocean. Forced ocean general circulation model (OGCM) simulations are successful in reproducing the spatial patterns of this intraseasonal SST variability albeit with a weaker amplitude. The weaker amplitude given by the OGCM is partly related to the absence of warm-layer formation in the model. The model simulation reveals that the background oceanic subsurface structure explains the observed latitudinal distribution of the SST perturbations. For the Indian Ocean, the Ekman pumping (reinforced in 1999 due to La Niña conditions) gives a thermocline close to the surface between 5Њ and 10ЊS that inhibits the deepening of the mixed layer during strong wind episodes and thus gives a mixed layer temperature more reactive to surface forcing. Other factors like the Ekman dynamics associated with the wind burst and the precipitation perturbation south of the equator also contribute toward preventing the deepening of the mixed layer. For these regions, as is found over the western Pacific, the intraseasonal variability of the SST is mainly driven by the surface fluxes perturbation, and not by advection or exchanges with the subsurface. As a consequence, the phasing and the magnitude of convective and large-scale dynamical perturbations of the surface fluxes, which are regionally dependent, are also determinant factors for the local amplitude of the SST perturbation. Finally, results show a relation at interannual time scales between the thermocline structure and the mixed layer depth south of the equator that may have consequences on interannual changes in the intraseasonal activity over the Indian Ocean.
ISI Document Delivery No.: 171QI Times Cited: 0 Cited Reference Count: 45 Cited References: ARNAUD Y, 1992, J APPL METEOROL, V31, P443, DOI 10.1175/1520-0450(1992)031<0443:ATACOA>2.0.CO;2 ASPLIDEN CI, 1976, MON WEATHER REV, V104, P1029, DOI 10.1175/1520-0493(1976)104<1029:SCAOWA>2.0.CO;2 BARNES GM, 1984, MON WEATHER REV, V112, P1782, DOI 10.1175/1520-0493(1984)112<1782:TEOFAS>2.0.CO;2 Boer E., 1997, J GEOPHYS RES, V102, p21 383 Bouniol D, 2010, Q J ROY METEOR SOC, V136, P323, DOI 10.1002/qj.557 Carvalho LMV, 2001, J APPL METEOROL, V40, P1683, DOI 10.1175/1520-0450(2001)040<1683:ASMTIS>2.0.CO;2 Desbois M, 1988, J CLIMATE, V1, P867, DOI 10.1175/1520-0442(1988)001<0867:COSEOT>2.0.CO;2 Diongue A, 2002, Q J ROY METEOR SOC, V128, P1899, DOI 10.1256/003590002320603467 DIXON M, 1993, J ATMOS OCEAN TECH, V10, P785, DOI 10.1175/1520-0426(1993)010<0785:TTITAA>2.0.CO;2 ENDLICH RM, 1981, J APPL METEOROL, V20, P309, DOI 10.1175/1520-0450(1981)020<0309:ACTATG>2.0.CO;2 Fiolleau T., 2012, Q J R METEOROL SOC Fiolleau T., 2012, GEOPH RES ABSTR, V14 Futyan JM, 2007, J CLIMATE, V20, P5041, DOI 10.1175/JCLI4297.1 Han L, 2009, J ATMOS OCEAN TECH, V26, P719, DOI 10.1175/2008JTECHA1084.1 Hodges KI, 1997, MON WEATHER REV, V125, P2821, DOI 10.1175/1520-0493(1997)125<2821:DASOAM>2.0.CO;2 HODGES KI, 1994, MON WEATHER REV, V122, P2573, DOI 10.1175/1520-0493(1994)122<2573:AGMFTA>2.0.CO;2 Houze R. A., 2004, REV GEOPHYS, V42 HOUZE RA, 1982, J METEOROL SOC JPN, V60, P396 HOUZE RA, 1981, REV GEOPHYS, V19, P541, DOI 10.1029/RG019i004p00541 Inoue T, 2009, J METEOROL SOC JPN, V87A, P381, DOI 10.2151/jmsj.87A.381 Johnson JT, 1998, WEATHER FORECAST, V13, P263, DOI 10.1175/1520-0434(1998)013<0263:TSCIAT>2.0.CO;2 Kondo Y, 2006, MON WEATHER REV, V134, P1581, DOI 10.1175/MWR3132.1 Lakshmanan V, 2009, J ATMOS OCEAN TECH, V26, P523, DOI 10.1175/2008JTECHA1153.1 Lakshmanan V, 2003, ATMOS RES, V67-8, P367, DOI 10.1016/S0169-8095(03)00068-1 Lakshmanan V, 2010, WEATHER FORECAST, V25, P701, DOI 10.1175/2009WAF2222330.1 Machado LAT, 2004, MON WEATHER REV, V132, P714, DOI 10.1175/1520-0493(2004)132<0714:TCSAEO>2.0.CO;2 MACHADO LAT, 1992, MON WEATHER REV, V120, P392, DOI 10.1175/1520-0493(1992)120<0392:SCODCS>2.0.CO;2 Machado LAT, 1998, MON WEATHER REV, V126, P1630, DOI 10.1175/1520-0493(1998)126<1630:LCVOMC>2.0.CO;2 MADDOX RA, 1980, B AM METEOROL SOC, V61, P1374, DOI 10.1175/1520-0477(1980)061<1374:MCC>2.0.CO;2 MAPES BE, 1993, MON WEATHER REV, V121, P1398, DOI 10.1175/1520-0493(1993)121<1398:CCASOT>2.0.CO;2 MARTIN DW, 1981, MON WEATHER REV, V109, P1671, DOI 10.1175/1520-0493(1981)109<1671:COWAAE>2.0.CO;2 Mathon V, 2001, Q J ROY METEOR SOC, V127, P377, DOI 10.1256/smsqj.57207 Meyer F., 1990, Journal of Visual Communication and Image Representation, V1, DOI 10.1016/1047-3203(90)90014-M Morel C., 1997, P MET SAT DAT US C B, P213 Redelsperger J. L., 1997, PHYS PARAMETERIZATIO, P159 Roca R, 2000, J CLIMATE, V13, P1286, DOI 10.1175/1520-0442(2000)013<1286:SDOMCS>2.0.CO;2 Roca R., 2002, J GEOPHYS RES, V107 Roca R, 2010, CR GEOSCI, V342, P390, DOI 10.1016/j.crte.2010.01.00...
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