No abstract

Spatial and temporal variations of atmospheric CO 2 concentrations contain information about surface sources and sinks, which can be quantitatively interpreted through tracer transport inversion. Previous CO 2 inversion calculations obtained differing results due to different data, methods and transport models used. To isolate the sources of uncertainty, we have conducted a set of annual mean inversion experiments in which 17 different transport models or model variants were used to calculate regional carbon sources and sinks from the same data with a standardized method. Simulated transport is a significant source of uncertainty in these calculations, particularly in the response to prescribed "background" fluxes due to fossil fuel combustion, a balanced terrestrial biosphere, and air-sea gas exchange. Individual model-estimated fluxes are often a direct reflection of their response to these background fluxes. Models that generate strong surface maxima near background exchange locations tend to require larger uptake near those locations. Models with weak surface maxima tend to have less uptake in those same regions but may infer small sources downwind. In some cases, individual model flux estimates cannot be analyzed through simple relationships to background flux responses but are

Spatial and temporal variations of atmospheric CO2 concentrations contain information about surface sources and sinks, which can be quantitatively interpreted through tracer transport inversion. Previous CO2 inversion calculations obtained differing results due to different data, methods and transport models used. To isolate the sources of uncertainty, we have conducted a set of annual mean inversion experiments in which 17 different transport models or model variants were used to calculate regional carbon sources and sinks from the same data with a standardized method. Simulated transport is a significant source of uncertainty in these calculations, particularly in the response to prescribed “background” fluxes due to fossil fuel combustion, a balanced terrestrial biosphere, and air–sea gas exchange. Individual model‐estimated fluxes are often a direct reflection of their response to these background fluxes. Models that generate strong surface maxima near background exchange locations tend to require larger uptake near those locations. Models with weak surface maxima tend to have less uptake in those same regions but may infer small sources downwind. In some cases, individual model flux estimates cannot be analyzed through simple relationships to background flux responses but are likely due to local transport differences or particular responses at individual CO2 observing locations. The response to the background biosphere exchange generates the greatest variation in the estimated fluxes, particularly over land in the Northern Hemisphere. More observational data in the tropical regions may help in both lowering the uncertain tropical land flux uncertainties and constraining the northern land estimates because of compensation between these two broad regions in the inversion. More optimistically, examination of the model‐mean retrieved fluxes indicates a general insensitivity to the prior fluxes and the prior flux uncertainties. Less uptake in the Southern Ocean than implied by oceanographic observations, and an evenly distributed northern land sink, remain in spite of changes in this aspect of the inversion setup.]

A systematic examination of the dynamical relationship between the North Atlantic Oscillation (NAO) and atmospheric blocking episodes in the North Atlantic during winter is undertaken. Employing the blocking criteria, as defined by Tibaldi and Molteni (1990), we first establish a statistical relationship, through compositing and linear regression analysis, between the two phenomena. The results show that the frequency of blocking formations in the North Atlantic region is sensitive to the phase of the NAO. Sixty-seven percent more winter blocking days are observed during the negative than the positive phase of the NAO. The lifetime of blocking episodes is also sensitive to the phase of the NAO. When the NAO is in the negative phase, the distribution of the length of blocking varies considerably. The average length of blocking during the negative phase is about 11 days, which is nearly twice as long as the 6-day length observed during the positive phase of the NAO. The NAO accounts for about 30% of the variation in the wintertime North Atlantic blocking episodes. We propose a conceptual model that strengthens the statistical association and offers an explanation for a dynamical connection between the occurrences of blocking and the NAO in the North Atlantic. Application of a low-order theoretical model by Charney and DeVore (1979) and an analysis of Northern Hemisphere observed surface temperature suggest that the NAO-related difference in blocking frequency and persistence are associated with changes in the zonally asymmetric thermal forcing which, to a large extent, is determined by the phase of the NAO. For the negative phase of the NAO, the distribution of the surface air temperature anomaly is the distinctive 'warm ocean/cold land' pattern related to the resonance forcing of topography and creates a dynamical environment favourable for the formation and persistence of blocks. For the positive phase of the NAO, on the other hand, the distribution of the surface air temperature anomalies is the distinctive 'cold ocean/warm land' pattern, which reduces or destroys the resonance forcing of topography and is unfavourable for the development and persistence of blocks.

Digital ®ltering and harmonic regression curve ®tting techniques are applied to CO 2¯a sk data from four stations in North America (Pt. Barrow, Alert, Sable Island and Cape St. James) to evaluate these two dierent methodologies in terms of growth rate and seasonal cycle in the atmospheric CO 2 concentration. Both methods agree relatively well in producing long-term atmospheric CO 2 trend at each of the monitoring stations, as well as in capturing relatively large interannual variations in the annual growth rate. Furthermore, they both agree in indicating the dependency of the variation in the seasonal amplitude on the seasonal minimum concentration. The digital ®ltering technique is able to capture the local temporal variation in CO 2 measurements much better than the harmonic regression method, although in some cases this variability is exaggerated in the digital ®ltering approach. The harmonic regression approach tends to smooth out the data, with much of the power in the very long period oscillations. Also, the timing of the occurrence of the seasonal minimum calculated by the digital ®ltering method tends to be earlier than that calculated by the harmonic regression method, although both methods do not indicate any major secular change in the timing. The overall assessment of the two methods applied to the CO 2¯a sk data underscores the importance of using more than one curve ®tting method before any conclusions can be drawn from thē ask data about the interannual variability in the trend and seasonal cycle of the atmospheric CO 2 concentration. #