Interannual variability

In an atmospheric inversion, the atmosphere acts as a consistency check between land and ocean estimates since their sum must match the growth rate and spatial gradient information in the concentration observations. This can be implemented by using the flux estimates as a prior constraint, with confidence dictated by the providers of the estimates. While the calculations here have not done this, experience shows that the estimated fluxes would be little different unless I used 

FIGURE 3 12-month running mean fluxes to the atmosphere (Gt C year ) for the ocean (a) and land (b) from the inversions of Rayner ct nl. 

There are many potential explanations for the inconsistency. The first is that the inversion misallocates variability between the land and ocean. This is certainly possible since the observing network is strongly biased toward the ocean. It is part of the behavior of such Bayesian inversions that they will adjust those fluxes that are best sampled to make up for a mismatch between data and the initial guess. Thus the inversion will propagate interannual variability in concentration data preferentially to the relatively well-sampled ocean rather than the poorly sampled land. This effect is reduced by the use of d C, a tracer that marks 

The final and most radical suggestion concerns a different way of modeling the problem. Partly the suggestion comes from reconsidering the question of why the carbon-cycle community is interested in interannual variability Wliile the task of estimating the interannual variability of fluxes is difficult enough to have become an end in itself, it is not sufficient. The next step is to elucidate those processes that control the variations in flux. This is a fascinating scientific question but also has practical import since it should enable 

To see how such an approach may work, recall that the current inversion methods involve an optimization problem, seeking to minimize a cost function, 1 , comprising a mismatch between modeled concentrations (D) and observed data (Do) plus another term for the mismatch between estimated sources (S) and an initial estimate of those sources (So), 

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Cycles established as statistically real are the familiar annual and diurnal radiation/temperature cycles, a quasibiennial (about every 2 years) fluctuation in various climatic elements, and the interannual variability of June rainfall in northern India. The first merely means that winters are cooler than summers and nights are cooler than days. Examples of the second cycle include Midwestern rainfall, a lengthy temperature record from central England, and winds over the western Paciflc and eastern Indian Ocean. According to Campbell et al (19), the third cycle may be a response to the monthly solar-lunar tide and its influence on the monsoon circulation. 

Rowell DP (2005) A scenario of European climate change for the late twenty-first century seasonal means and interannual variability. Clim Dyn 25 837-849. doi 10.1007/s00382-005-... 

Also, the alterations can be classified depending on their intensity. The more intense or frequent the intensity, the easier to predict the consequences. When the alterations are slighter or less frequent, it may occur that the natural factors are more important than the alteration itself [3]. The effect produced by the reservoirs depends on different factors, like the size of the reservoir, the residence time, the stability of the thermal stratification and the withdrawal depth. Moreover, there is a certain interannual variability in the magnitude as well as in the timing of the alterations [4]. Among these factors, the most important one is the depth at which water is released. 

Y. Dandonneau, P.-Y. Deschamps, J.-M Nicolas, H. Loisel, J. Blanchot, Y. Montel, F. Thieuleux and G. Becu, Seasonal and interannual variability of ocean color and composition of phytoplankton communities in the North Atlantic, equatorial Pacific and South Pacific. Deep Sea Res. II 51 (2004) 303-318. 

Karl, D.M., J.R. Christian, J.E. Dore, D.V. Hebei, R.M. Letelier, L.M. Tupas, and C.D. Winn. 1996. Seasonal and interannual variability in primary production and particle flux at Station ALOHA. Deep-Sea Research II 43 1270-1286. 

Fig. 15 Interannual variability of runoff regimes from 1993 to 2006, based on classified Parde coefficients of selected catchment areas at varying mean altitudes [56]...

Garcia, R. R and S. Solomon, A Possible Relationship between Interannual Variability in Antarctic Ozone and the Quasi-biennial Oscillation, Geophys. Res. Lett., 14, 848-851 (1987). 

Gleason, J.F., N. Christina Hsu and O. Torres (1998) Biomass burning smoke measured using backscattered ultraviolet radiation SCAR-B and Brazilian smoke interannual variability. Journal of Geophysical Research (in press). 

Spliethoff, H.M., Mason, R.P. and Hemond, H.F. (1995) Interannual variability in the speciation and mobility of arsenic in a dimictic lake. Environmental Science and Technology, 29(8), 2157-161. 

Vuille M, Bradley RS, Werner M, Healy R, Keimig F (2003) Modeling 8180 in precipitation over the tropical Americas 1. Interannual variability and climatic controls. J Geophys Res 108 doi 10.1029/ 2001JD002038... 

Hoch, M. P., and D. L. Kirchman. 1993. Seasonal and interannual variability in bacterial production and biomass in a temperate estuary. Marine Ecology Progress Series 98 283-295. 

Lafleur, P. M., Roulet, N. T., Bubier, J. L., Frolking, S., and Moore, T. R. (2003). Interannual variability in the peatland-atmosphere carbon dioxide exchange at an ombrotrophic bog. Global Biogeochem. Cycles 17,1036. 

Long-term fluctuations in lipid accumulation by Black Sea sprat during the summer post-feeding period, when their fat reserve is at its peak, were observed from 1960 to 1994 and this work continues (Shulman et al.t 1994). Interannual variability in the lipid content, though small, is real, with a coefficient of variation of 12-15%, as estimated at certain points of this period of time. 

Considerable progress in modeling the interactive atmosphere-ocean system has made it possible to successfully predict seasonal and interannual variability and, in particular, El Nino events. The sufficiently adequate consideration of land surface processes ensured a substantial increase in hydrological prediction reliability (river run-off included). 

In this context, Bengtsson (1999) discussed three directions taken in progressing numerical modeling of the climate. Successful accomplishment of the TOGA program promoted operational predictions of seasonal and interannual variability with prescribed SST changes in the tropics taken into account, which determined the critical significance of reliable SST data. 

Regional transfer of carbon in 2000 due to production of and trade in crops, wood, and paper constituted 0.72 GtC yr 1. The pure global carbon flux at the atmosphere-ocean boundary in 1995 was estimated at 2.2 GtC (—19% +22%) with an interannual variability of about 0.5 GtC. The greatest extent of C02 flux oscillations in the system can be observed in the equatorial Pacific. 

Analysis of 7 to 8-year oscillations revealed a contrast in their phases in the subtropical and sub-polar cycles of the northern Atlantic. Three prevailing periods have been recorded for interannual variability (2-6 years) 24-30 months, 40 months, and 60-65 months. The first of these periodicities is the well-known quasi-2-year component of ENSO which manifests itself most strongly in the tropics of the western sector of the Pacific, with anomalies (of a constant sign) propagating along the western coast of North and South America (in other oceans this variability is negligibly small). 

What are the uncertainties in predicting annual change and interannual variability as well as in making long-term projections of various parameters (components) of the water cycle and what are the possibilities of reducing the levels of these uncertainties ... 

Grytsai A. Grytsai Z. Evtushevsky A. and Milinevsky G. (2005). Interannual variability of planetary waves in the ozone layer at 65 degrees S. Int. JRemote Sens., 26(16), 3377-3387. 

Ichii K. Hashimoto H. Nemani R. and White M. (2005). Modeling the interannual variability and trends in gross and net primary productivity of tropical forests from 1982 to 1999. Global and Planetary Change, 48(4), 274-286. 

Johnson C.E. Stevenson D.S. Collins W.J. and Derwent R.G. (2002). Interannual variability in methane growth rate simulated with a coupled Ocean-Atmosphere-Chemistry model. Geophysical Research Letters, 29(19), 1-4. 

Rogers A.N. Bromwich D.H. Sinclair E.N. and Cullather R.I. (2001). The atmospheric hydrologic cycle over the Arctic Basin from reanalyses. Part 2. Interannual variability. Journal of Climate, 14(11), 2414 -2429. 

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