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[1] Owing to global spatial sampling and sheer data volume, satellite CO 2 concentrations can be used in inverse models to enhance our understanding of the carbon cycle. Using column measurements to represent a transport model grid column may introduce spatial, local clear-sky, and temporal sampling errors into inversions: the footprint is smaller than a grid cell, total column concentrations are only retrieved in clear skies, and the mixing ratios are only sampled at one time. To investigate these errors, we used a coupled ecosystem-atmosphere cloud-resolving model to create CO 2 fields over fine ($1°Â 1°) and coarse ($4°Â 4°) grid columns from 1 km 2 and 25 km 2 pixels that utilized explicit microphysics. We performed two simulations in August 2001: one in central North America and one in the Brazilian Amazon. Differences between satellite and grid column concentrations were calculated by subtracting the domain mean column concentration from 10-km-wide simulated satellite measurements. Spatial and local clear-sky errors were less than 0.5 ppm for the fine grid column; however, these errors became large and biased over the coarse grid column in North America. To avoid these errors, transport models should be run at high resolution. Using satellite measurements to represent bimonthly averages created large (>1 ppm) errors for all cases. The errors were negatively biased (approximately À0.4 ppm) in the North American simulation, indicating that inverse models cannot use satellite measurements to represent temporal averages. Simulated representation errors did not arise because of differences in ecosystem metabolism in cloudy versus sunny conditions; rather, they reflected large-scale CO 2 gradients in midlatitudes that were organized along frontal boundaries and masked under regional cloud cover. Such boundaries were not found in the dry-season tropical simulation presented here and may be less prevalent in the tropics in general. To avoid incurring errors, inversions must accurately model synoptic-scale atmospheric transport and CO 2 concentrations must be assimilated at the time and place observed.

[1] The effects of solar radiation diurnal cycle on intraseasonal mixed layer variability in the tropical Indian Ocean during boreal wintertime Madden-Julian Oscillation (MJO) events are examined using the HYbrid Coordinate Ocean Model. Two parallel experiments, the main run and the experimental run, are performed for the period of 2005-2011 with daily atmospheric forcing except that an idealized hourly shortwave radiation diurnal cycle is included in the main run. The results show that the diurnal cycle of solar radiation generally warms the Indian Ocean sea surface temperature (SST) north of 10 S, particularly during the calm phase of the MJO when sea surface wind is weak, mixed layer is thin, and the SST diurnal cycle amplitude (dSST) is large. The diurnal cycle enhances the MJO-forced intraseasonal SST variability by about 20% in key regions like the Seychelles-Chagos Thermocline Ridge (SCTR; 55 -70 E, 12 -4 S) and the central equatorial Indian Ocean (CEIO; 65 -95 E, 3 S-3 N) primarily through nonlinear rectification. The model also well reproduced the upper-ocean variations monitored by the CINDY/DYNAMO field campaign between September-November 2011. During this period, dSST reaches 0.7 C in the CEIO region, and intraseasonal SST variability is significantly amplified. In the SCTR region where mean easterly winds are strong during this period, diurnal SST variation and its impact on intraseasonal ocean variability are much weaker. In both regions, the diurnal cycle also has a large impact on the upward surface turbulent heat flux Q T and induces diurnal variation of Q T with a peak-to-peak difference of O(10 W m À2 ).