28 research outputs found
The role of air–sea coupling on November–April intraseasonal rainfall variability over the South Pacific
We investigate the impact of resolving air-sea interaction on the simulation of the intraseasonal rainfall variability over the South Pacific using the ECHAM5 atmospheric general circulation model coupled with the Snow-Ice-Thermocline (SIT) ocean model. We compare the fully coupled simulation with two uncoupled ECHAM5 simulations, one forced with sea surface temperature (SST) climatology and one forced with daily SST from the coupled model. The intraseasonal rainfall variability over the South Pacific is reduced by 17% in the uncoupled model forced with SST climatology and increased by 8% in the uncoupled simulation forced with daily SST, suggesting the role of air–sea coupling and SST variability. The coupled model best simulates the key characteristics of the two dominant patterns (modes) of intraseasonal rainfall variability over the South Pacific with reasonable propagation and correct periodicity. The spatial structure of the two rainfall modes in all three simulations is very similar, suggesting the dynamics of the atmosphere primarily generate these modes. The southeastward propagation of rainfall anomalies associated with two leading rainfall modes in the South Pacific depends upon the eastward propagating Madden–Julian Oscillation (MJO) signals from the Indian Ocean and western Pacific. Air-sea interaction improves such propagation as both eastward and southeastward propagations are substantially reduced in the uncoupled model forced with SST climatology. The simulation of both eastward and southeastward propagations considerably improved in the uncoupled model forced with daily SST; however, the periodicity differs from the coupled model. Such discrepancy in the periodicity is attributed to the changes in the SST-rainfall relationship with weaker correlations and the nearly in-phase relationship, attributed to enhanced positive latent heat flux feedbacks.publishedVersio
An Overview of Regional Experiments on Biomass Burning Aerosols and Related Pollutants in Southeast Asia: From BASE-ASIA and the Dongsha Experiment to 7-SEAS
By modulating the Earth-atmosphere energy, hydrological and biogeochemical cycles, and affecting regional-to-global weather and climate, biomass burning is recognized as one of the major factors affecting the global carbon cycle. However, few comprehensive and wide-ranging experiments have been conducted to characterize biomass-burning pollutants in Southeast Asia (SEA) or assess their regional impact on meteorology, the hydrological cycle, the radiative budget, or climate change. Recently, BASEASIA (Biomass-burning Aerosols in South-East Asia: Smoke Impact Assessment) and the 7-SEAS (7- South-East Asian Studies) Dongsha Experiment were conducted during the spring seasons of 2006 and 2010 in northern SEA, respectively, to characterize the chemical, physical, and radiative properties of biomass-burning emissions near the source regions, and assess their effects. This paper provides an overview of results from these two campaigns and related studies collected in this special issue, entitled Observation, modeling and impact studies of biomass burning and pollution in the SE Asian Environment. This volume includes 28 papers, which provide a synopsis of the experiments, regional weatherclimate, chemical characterization of biomass-burning aerosols and related pollutants in source and sink regions, the spatial distribution of air toxics (atmospheric mercury and dioxins) in source and remote areas, a characterization of aerosol physical, optical, and radiative properties, as well as modeling and impact studies. These studies, taken together, provide the first relatively complete dataset of aerosol chemistry and physical observations conducted in the sourcesink region in the northern SEA, with particular emphasis on the marine boundary layer and lower free troposphere (LFT). The data, analysis and modeling included in these papers advance our present knowledge of source characterization of biomass-burning pollutants near the source regions as well as the physical and chemical processes along transport pathways. In addition, we raise key questions to be addressed by a coming deployment during springtime 2013 in northern SEA, named 7-SEASBASELInE (Biomass-burning Aerosols Stratocumulus Environment: Lifecycles and Interactions Experiment). This campaign will include a synergistic approach for further exploring many key atmospheric processes (e.g., complex aerosol-cloud interactions) and impacts of biomass burning on the surface-atmosphere energy budgets during the lifecycles of biomass burning emissions
Resolving the upper-ocean warm layer improves the simulation of the Madden-Julian oscillation
Here we show that coupling a high-resolution one-column ocean model to an atmospheric general circulation model dramatically improves simulation of the Madden–Julian oscillation (MJO) to have realistic strength, period, and propagation speed. The mechanism for the simulated MJO involves both frictional wave-convective conditional instability of the second kind (Frictional wave-CISK) and air–sea convective intraseasonal interaction (ASCII). In particular, better resolving the fine structure of upper ocean temperature, especially the warm layer, produces more vigorous atmosphere–ocean interaction and strengthens intraseasonal variations in both SST and atmospheric circulation. This helps organize and strengthen deep convection, inducing a stronger Kelvin-wave like perturbation and frictional near-surface convergence to the east. In addition, the warmer SST ahead of the MJO also acts to destabilize the boundary layer and enhance frictional convergence. These lead to a more realistic eastward-propagating MJO. A suite of sensitivity experiments were performed to show the robustness of the mechanisms and to demonstrate: (1) that mean state differences are not the main contributors to the improved simulation of our coupled model; (2) the role of SST variability in enhancing frictional convergence and intraseasonal variations in precipitation, and (3) that the simulation is significantly degraded when the first ocean model layer is thicker than 10 m. Our coupled model results are consistent with observations and demonstrate a simple but effective means to significantly improve MJO simulation and potentially also forecasts
Using a Photochemical Index to Discuss the Ozone Formation and Estimate Hydroxyl Concentration at Downwind Area
Quantification of climatic feedbacks on the Caspian Sea level variability and impacts from the Caspian Sea on the large-scale atmospheric circulation
International audienc
Vertical Profile of Ozone and Accompanying Air Pollutant Concentrations Observed at a Downwind Foothill Site of Industrial and Urban Areas
The role of air–sea coupling on November–April intraseasonal rainfall variability over the South Pacific
We investigate the impact of resolving air-sea interaction on the simulation of the intraseasonal rainfall variability over the South Pacific using the ECHAM5 atmospheric general circulation model coupled with the Snow-Ice-Thermocline (SIT) ocean model. We compare the fully coupled simulation with two uncoupled ECHAM5 simulations, one forced with sea surface temperature (SST) climatology and one forced with daily SST from the coupled model. The intraseasonal rainfall variability over the South Pacific is reduced by 17% in the uncoupled model forced with SST climatology and increased by 8% in the uncoupled simulation forced with daily SST, suggesting the role of air–sea coupling and SST variability. The coupled model best simulates the key characteristics of the two dominant patterns (modes) of intraseasonal rainfall variability over the South Pacific with reasonable propagation and correct periodicity. The spatial structure of the two rainfall modes in all three simulations is very similar, suggesting the dynamics of the atmosphere primarily generate these modes. The southeastward propagation of rainfall anomalies associated with two leading rainfall modes in the South Pacific depends upon the eastward propagating Madden–Julian Oscillation (MJO) signals from the Indian Ocean and western Pacific. Air-sea interaction improves such propagation as both eastward and southeastward propagations are substantially reduced in the uncoupled model forced with SST climatology. The simulation of both eastward and southeastward propagations considerably improved in the uncoupled model forced with daily SST; however, the periodicity differs from the coupled model. Such discrepancy in the periodicity is attributed to the changes in the SST-rainfall relationship with weaker correlations and the nearly in-phase relationship, attributed to enhanced positive latent heat flux feedbacks
Determination of methane and carbon dioxide fluxes during the rice maturity period in Taiwan by combining profile and eddy covariance measurements
A field campaign was undertaken over a rice paddy in Central Taiwan to investigate the flux of CH4 through the flux-gradient method. During the campaign period, the average observed concentrations of CH4 during the campaign period at the lower height (22.2 m above ground level, agl) and upper height (27.5 m agl) were around 2.04 ppm and 2.01 ppm, respectively. It was noticed that the profile concentration of CH4 was lower during day time when compared to night time. However, the flux of CH4 was observed to be higher during the day time than during night hours. In order to understand the relationship between the influences of meteorological variables on the flux of CH4, six meteorological variables were studied. It was observed that soil heat flux has a linear positive correlation with the flux of CH4. During the study period, the maturity stage of rice paddy, the rice paddy ecosystem was behaving as sources of CH4 and CO2. Over a 100-year time frame, the global warming potential (GWP) fluxes of CH4 and CO2 observed at this study site were 0.16 and 0.71 μmol CO2 equivalents m−2 s−1, respectively. The total GWP flux (CH4 and CO2) was 0.86 μmol CO2 equivalents m−2 s−1 in which CH4 and CO2 contribute 18% and 82%, respectively