[1] Synoptic events may play an important role in determining the CO 2 spatial distribution and temporal variations on a regional scale. In this study, we chose a front that passed the WLEF tower site on 16 August 2001, which had the most significant CO 2 concentration variation in our case pool. The CO 2 concentration, or [CO 2 ], at the WLEF site had a strong dip and an increasing trend before the front arrived and a decreasing trend afterward. The concentration at 396 m above the ground varied by more than 40 ppm within 36 hours. We investigated the CO 2 variations associated with this frontal case using a fully coupled model of land surface physics and carbon exchange (SiB 2.5) and the atmosphere (RAMS 5.04), in which CO 2 was treated as a free variable and used to determine photosynthesis rate. under overcast sky condition were also partially responsible for the quick CO 2 accumulation at the lower levels at the WLEF site before the front's arrival. This case study confirmed the existence of mixing signals from at least two different scales: large-scale horizontal advection and local ecosystem response to the changing weather. SiB-RAMS showed its strength in simulating the coherent anomalies in biospheric CO 2 flux and in the regional weather pattern. Further refinement of the model is needed to better capture the timing and location of synoptic events and CO 2 signals that travel across North America. Exploitation of continuous tower data in data assimilation and inverse modeling to determine regional sources and sinks will require careful error attribution to either transport or surface flux estimates.

[1] The impacts of two consecutive, strong tropical cyclones (TCs) from October-November in 1999 on the Bay of Bengal (BoB) heat budget are examined using the Hybrid Coordinate Ocean Model. The model uses atmospheric conditions from reanalysis, reconstructed TC winds, and satellite-observed winds and precipitation. We conduct a series of diagnostic experiments to isolate the model's response to the individual TC-associated forcings. During the TCs, the BoB ocean heat content (OHC) is reduced, primarily due to TC-wind induced southward ocean heat transport (OHT) and a reduction in surface downward radiation due to increased cloudiness. BoB OHC is largely restored in the following months via enhanced surface heat fluxes, associated with cold wake restoration, and positive northward OHT. The TCs' downward heat pumping effect is estimated to be $1.74 Â 10 18 J near the end of February 2000, which is less than estimates using previously published methods based on surface observations. The relatively weak heat pumping results from freshwater input by intense monsoon rainfall and river discharge in the BoB, which stabilizes stratification, forms a barrier layer, and generates temperature inversions during seasonal surface cooling. As a result, early stage TC winds entrain the warm barrier layer water and enhance enthalpy loss in the southeastern Bay, while mature stage TC winds erode the barrier layer, decrease SST through upwelling and entrainment of deeper cold water and reduce enthalpy loss in the northwestern Bay. Our findings suggest TC winds may significantly alter the interseasonal BoB heat budget through OHT and surface heat fluxes.

[1] The impacts of two consecutive, strong tropical cyclones (TCs) -04B (10/15-10/19) and 05B (10/25-11/3) in 1999 (hereafter, TC1 and TC2) -on the upper ocean temperature and surface height of the Bay of Bengal (BoB) are examined using the Hybrid Coordinate Ocean Model (HYCOM). The HYCOM control run is driven by the Cross-Calibrated MultiPlatform (CCMP) satellite winds, European Center for Medium-Range Weather Forecasts Re-analysis Interim (ERAI) surface reanalysis data, and Tropical Rainfall Measuring Mission precipitation. In order to investigate ocean response to high wind conditions, which are not well resolved by the CCMP or ERAI products, a modified Rankine vortex is adopted to reconstruct the TC winds in an experimental run. Wind stress is determined from wind speed when considering the level-off and decline of drag coefficient at wind speed of 34 m/s and greater. The experimental run reproduces the strong SST reduction ($À3 C) near the Orissa seashore along and on the right of the TC tracks. TC2 (category 5) cools the BoB SST less than TC1 (category 4) likely due to the initial SST depression by TC1. TC2 has higher winds and lingers over the ocean longer than TC1, and hence the onshore Ekman transport and mass convergence induced by TC2 wind is more prominent. The HYCOM mixed layer temperatures and depths to the south of the BoB generally agree with the observations very well. The simulations, however, have weaker vertical temperature gradient in the thermocline layer, suggesting that HYCOM produces a more diffusive thermocline than the observations.

[1] An increase in the atmospheric moist content has been generally assumed when the lower-tropospheric temperature (T col ) increases, with relative humidity holding steady. Rather than using simple linear regression, we propose a more rigorous trend detection method that considers time series memory. The autoregressive moving-average (ARMA) parameters for the time series of T col , precipitable water vapor (PWAV), and total precipitable water content (PWAT) from the North American Regional Reanalysis data were first computed. We then applied the Monte Carlo method to replicate the ARMA time series samples to estimate the variances of their Ordinary Least Square trends. Student's t tests showed that T col from 1979 to 2006 increased significantly; however, PWAV and PWAT did not. This suggests that atmospheric temperature and water vapor trends do not follow the conjecture of constant relative humidity over North America. We thus urge further evaluations of T col , PWAV, and PWAT trends for the globe.

Future meteorological satellites are expected to provide much needed fine‐scale information that can improve the accuracy of weather and climate models. As one application of this improved capability, we introduce the concept of a generalized parameterization framework using satellite datasets that will increase the accuracy and the computational efficiency of weather and climate modeling. In an atmospheric model, several different parameterizations usually are used to reproduce the various physical processes. However, it is generally unrealistic to separate the processes in this artificial way since the observations and physics make no such artificial separation. Thus, we propose a new unified parameterization framework to remove the unrealistic separation between parameterizations.