The atmosphere is responsible for a number of key processes that humans depend upon, this includes heat transport and planetary thermal regulation and the distribution of freshwater through clouds and cloud formation. These processes are governed by the incoming solar radiation and the relative amount of gas constituents available. The spatial patterns of differences in gas mixtures (both horizontally and vertically) and changes in these patterns are an important area of research.

Monitoring of key gas groups can be done on a large scale through the use of remote sensing techniques such as limb sounding. As with all space-based Earth Observation (EO), the need for derived products (e.g. column profiles of water vapour/sulphur dioxide, etc.) with an estimate of its quality is paramount. The characterisation and operationalisation of references standards for limb-sounding instruments (e.g. large aperture reference standards – LARS) in this project is set to allow significant progress towards this goal.

A secondary reason for studying the atmosphere in our project is to enable the removal of its effects when conducting research over the land, oceans or ice. In order to achieve this, well-validated radiative transfer models (RTMs) such as Second Simulation of a Satellite Signal in the Solar Spectrum (6S – Vermote et al., 1997) and MODerate resolution atmospheric TRANsmission (MODTRAN – Berk et al., 2006) are used to determine the way the signal has been altered by the atmosphere as it propagates from the sun to the Earth to the sensor’s aperture. These models work based on the provision of meteorological data provided by the user but also allow the use of a set of pre-defined atmospheres where data availability is lacking.

These models are highly complex but there are two things that need to be addressed by the metrology community 1) propagation of uncertainties through the RTMs and, 2) quantification of uncertainty in the input parameters. Tackling these will reduce uncertainty for land-, ocean- and ice- based research applications.


The land surface plays an important role in the carbon cycle but accounts for the largest uncertainty in carbon uptake, this is shown in figure 1 where a variation of 3.4 Gt carbon per year is seen (Quegan, 2009). Similarly, Figure 1 also shows that a significant source of carbon is stored in the Earth’s vegetation making this a critical component of the global carbon budget.


Figure 1. The global carbon cycle; values given in Gt carbon per year; values found in IPCC AR4 (2007), figure from Quegan (2009).

The importance of carbon stems from its reaction with oxygen at the various (i.e. land/ocean/ice) boundaries with the atmosphere. This creates CO2 (carbon dioxide) which is key radiative forcing and remains in the atmosphere for a number of years (see here).

Quantifying the amount of carbon stored in, and emerging from, vegetated ecosystems is a pressing issue with a numerous scientific groups devoted to its study; this was highlighted recently with the selection of the BIOMASS mission by the European Space Agency (ESA), a P-band synthetic aperture radar (SAR) sensor, devoted to providing frequent global estimates of forest carbon stocks.

In order to retrieve biomass (and subsequently carbon stock) estimates from SAR data, a complex set of processing steps must be followed. Unlike in the optical domain, there is a lack of ready-to-use products derived from SAR data. In the TruDAT project, a SAR processing chain was produced, with uncertainty propagation, which allows the user to obtain quality assured SAR data in the form of the backscatter coefficient, which is broadly related to biomass.

Estimates of carbon stock and flux can also be derived from regional relationships with leaf area index (LAI) or through modelling photosynthesis. The latter approach allows the prediction of carbon emissions and uptake as a function of changing environmental conditions (e.g. temperature, rainfall, etc.). Optical instruments are also used to derive estimates of land use and land use change, a component of MRV (monitoring, reporting and verification) systems that will underpin any future carbon trading schemes, as well as a plethora of other purposes.


Understanding the oceans is a crucial part of understanding the Earth. Oceans play a key role in climate as they provide a fundamental energy source driving weather patterns over the Earth, and also play a key role to sustainability via the food chain and towards an understanding of the carbon cycle. NPL is looking at a number of essential climate variables related to ocean processes, ocean colour and sea surface temperature.



Source: NASA

Oceans cover 70% of our planet and are the primary driver of weather and climate. They contain 50 times more carbon than the atmosphere and 20 times more than the land. At least half the oxygen produced globally comes from marine photosynthesis, and at least half of the CO2 released from fossil fuel burning is sequestered by the oceans. Even though they play such a critical role in the Earth’s system and in the regulation of our climate, the oceans remain the least explored of our environments. In an age of varying and changing climate, it is therefore of high priority that efforts continue to be made to more completely understand the oceanic environment, its function as part of the Earth’s system and monitor any changes that may be occurring. This can only be achieved through comprehensive and robust measurement at regional and global scales.

Spaceborne earth observation enables evidence-based global monitoring and has revolutionised physical and biological oceanography, giving us the means to measure the oceans synoptically with good repeat coverage. This has allowed us to observe connections within the system that would otherwise have been difficult to recognise from in-situ measurements alone. However, the ability to reliably detect trends from a background of natural variability requires decades of measurements, each with robust uncertainty estimates. MetEOC is helping to address this problem by contributing to improvements in satellite and in situ oceanographic measurements through the provision of SI-traceability, end-to-end uncertainty evaluation and a resultant increase in reliability and robustness of measurement for satellite oceanography.

Radiation Balance

Climate change is caused, at a fundamental level, by changes in the proportion of incoming (total solar irradiance – TSI) to outgoing (thermal) radiation. The environmental effects of such changes in the radiation balance range in impact but so-called ‘tipping points’ exist whereby small changes can cause a disproportionately large response.

TSI, as measured by a number of spaceborne instruments, has a data record of around 40 years. The results show a large variance of around 0.3% brought about by differences in the sun’s output as well as the instrument design. Consequently, the data record must be normalised for changes in instrumentation thereby increasing the uncertainty. Another ramification is that instrument failure, as with NASA’s GLORY mission, jeopardises the utility of the entire dataset. The latter issue may only be removed when the deployment of SI traceable sensors is realised; until then, the goal is achieve full SI traceability for ground TSI sensors using Cryogenic Solar Absolute Radiometers (CSARs) developed in MetEOC.

The ocean and land surface temperature account for most of the Earth’s outgoing radiation. This results from the Earth absorbing shortwave radiation and re-radiating it at longer (thermal) wavelengths. Sea surface temperature (SST) has a long history with some records close to a century long; the spaceborne datasets are only a third of this, with accurate measurements coming in the early 1990s from the ATSR series. This ended in 2012 with the failure of Envisat and is not due to be replaced until ESA’s follow on sensor, SLSTR, is launched on Sentinel-3 in 2015. SLSTR’s measurement capabilities will extend to land surface temperature (LST) when orbiting over the Earth’s land masses.


Vermote, E.F., Tanré, D., Deuzé, J.L., Herman, M., and Morcette, J.-J. (1997) Second Simulation of the Satellite Signal in the Solar Spectrum, 6S: An Overview. IEEE Transactions on Geoscience and Remote Sensing, 35(3): 675-686.

Berk, A., G.P. Anderson, P.K. Acharya, L.S. Bernstein, L. Muratov, J. Lee, M. Fox, S.M. Adler-Golden, J.H. Chetwynd, M.L. Hoke, R.B Lockwood, J.A. Gardner, T.W. Cooley, C.C. Borel, P.E. Lewis and E.P. Shettle (2006) MODTRAN5: 2006 Update. In: Proc. SPIE, Vol. 6233, 62331F.

IPCC, 2007: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M. Tignor and H.L. Miller (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA (link).

S. Quegan (2009) BIOMASS: ESA User Consultation Meeting, Lisbon, Portugal, 20-21 Jan 2009