Uncertainties in Climate Means cause state of Flux

"In the descriptions of the measurement process, so essentially simple, one can notice a significant reticence in many courses of mechanics and physics which have become classic. It was my task to establish more determinacy in the problem and, along with that, to show what a great arbitrariness is present in establishing a measurement" (Friedmann 1965, p.16).

The climate sciences are dominated by measurements to show “trends” in variability over time that correlates with rises in anthropogenic GHG that in turn correlate with changes in temperature.

Over 70% of the earth is covered by ocean an the importance of both sea temperatures and fluxes as inputs govern the climate models in qualitative and quantitative attributes.

Indeed the outputs are governed by the simplistic statement sensitive to initial conditions, where a degree of arbitrariness is present in providing datasets to provide global extrapolation of flux measurement.

Gulev et al in the Journal of climate identify some substantial problems with the measurement sampling for historical ocean-air flux interactions.

Estimation of the Impact of Sampling Errors in the VOS Observations on Air–Sea
Fluxes. Part I: Uncertainties in Climate Means

Gulev et al JOURNAL OF CLIMATE VOLUME 20 January 2007

ABSTRACT
Sampling uncertainties in the voluntary observing ship (VOS)-based global ocean–atmosphere flux fields were estimated using the NCEP–NCAR reanalysis and ECMWF 40-yr Re-Analysis (ERA-40) as well as seasonal forecasts without data assimilation. Air–sea fluxes were computed from 6-hourly reanalyzed individual variables using state-of-the-art bulk formulas. Individual variables and computed fluxes were subsampled to simulate VOS-like sampling density. Random simulation of the number of VOS observations and simulation of the number of observations with contemporaneous sampling allowed for estimation of random and total sampling uncertainties respectively. Although reanalyses are dependent on VOS, constituting an important part of data assimilation input, it is assumed that the reanalysis fields adequately reproduce synoptic variability at the sea surface. Sampling errors were quantified by comparison of the regularly sampled (i.e., 6 hourly) and subsampled monthly fields of surface variables and fluxes. In poorly sampled regions random sampling errors amount to 2.5°–3°C for air temperature, 3 m s 1 for the wind speed, 2–2.5 g kg 1 for specific humidity, and 15%–20% of the total cloud cover. The highest random sampling errors in surface fluxes were found for the sensible and latent heat flux and range from 30 to 80 Wm 2. Total sampling errors in poorly sampled areas may be higher than random ones by 60%. In poorly sampled subpolar latitudes of the Northern Hemisphere and throughout much of the Southern Ocean the total sampling uncertainty in the net heat flux can amount to 80–100 W m 2. The highest values of the uncertainties associated with the interpolation/ extrapolation into unsampled grid boxes are found in subpolar latitudes of both hemispheres for the turbulent fluxes, where they can be comparable with the sampling errors. Simple dependencies of the sampling errors on the number of samples and the magnitude of synoptic variability were derived. Sampling errors estimated from different reanalyses and from seasonal forecasts yield qualitatively comparable spatial patterns, in which the actual values of uncertainties are controlled by the magnitudes of synoptic variability. Finally, estimates of sampling uncertainties are compared with the other errors in air–sea fluxes and the reliability of the estimates obtained is discussed.