Improved understanding and systematic monitoring of Earth’s climate requires observations of various atmospheric, oceanic and terrestrial parameters and therefore has to rely on various technologies (ranging from ground-based instruments to ships, buoys, ocean profilers, balloons, aircraft, satellite-borne sensors, etc.). The Global Climate Observing System (GCOS, 2009) defined a list of so-called Essential Climate Variables, that are technically and economically feasible to observe, but some of the associated observing systems are not yet operated in a systematic manner. However, during recent years, new observational systems have increased the number of observations by orders of magnitude and observations have been made at places where there have been no data before (see Chapters 2, 3 and 4 for an assessment of changes in observations). Parallel to this, tools to analyse and process the data have been developed and enhanced to cope with the increase of information and to provide a more comprehensive picture of the Earth’s climate. At the same time, it should be kept in mind that there has been some limited progress in developing countries in filling gaps in their in situ observing networks, but developed countries have made little progress in ensuring long-term continuity for several important observing systems (GCOS, 2009). In addition, more proxy (non-instrumental) data have been acquired to provide a more comprehensive picture of climate changes in the past (see Chapter 5). Efforts are also occurring to digitize historic observations, mainly of ground-station data from periods prior to the second half of the 20th century (Brunet and Jones, 2011).
Reanalysis is a systematic approach to produce gridded dynamically consistent data sets for climate monitoring and research by assimilating all available observations with help of a climate model (Box 2.3). Model-based reanalysis products play an important role in obtaining a consistent picture of the climate system. However, their usefulness in detecting long-term climate trends is currently limited by changes over time in observational coverage and biases, linked to the presence of biases in the assimilating model (see also Box 2.3 in Chapter 2). Because AR4 both the quantity and quality of the observations that are assimilated through reanalysis have increased (GCOS, 2009). As an example, there has been some overall increase in mostly atmospheric observations assimilated in European Centre for Medium-Range Weather Forecasts Interim Reanalysis since 2007 (Dee et al., 2011). The overwhelming majority of the data, and most of the increase over recent years, come from satellites (Figure 1.12) (GCOS, 2011). For example, information from Global Positioning System radio occultation measurements has increased significantly since 2007. The increases in data from fixed stations are often associated with an increased frequency of reporting, rather than an increase in the number of stations. Increases in data quality come from improved instrument design or from more accurate correction in the ground-station processing that is applied before the data are transmitted to users and data centres. As an example for in situ data, temperature biases of radiosonde measurements from radiation effects have been reduced over recent years. The new generation of satellite sensors such as the high spectral resolution infrared sounders (such as the Atmospheric Infrared Sounder and the Infrared Atmospheric Sounding Interferometer) are instrumental to achieving a better temporal stability for recalibrating sensors such as the High-Resolution Infrared Radiation Sounder. Few instruments (e.g., the Advanced Very High Resolution Radiometer) have now been in orbit for about three decades, but these were not originally designed for climate applications and therefore require careful re-calibration.
A major achievement in ocean observation is due to the implementation of the Argo global array of profiling floats system (GCOS, 2009). Deployment of Argo floats began in 2000, but it took until 2007 for numbers to reach the design target of 3000 floats. Since 2000 the icefree upper 2000 m of the ocean have been observed systematically for temperature and salinity for the first time in history, because both the Argo profiling float and surface drifting buoy arrays have reached global coverage at their target numbers (in January 2009, there were 3291 floats operating). Biases in historical ocean data have been identified and reduced, and new analytical approaches have been applied (e.g., Willis et al., 2009). One major consequence has been the reduction of an artificial decadal variation in upper ocean temperature and heat content that was apparent in the observational assessment for AR4 (see Section 3.2). The spatial and temporal coverage of biogeochemical measurements in the ocean has also expanded. Satellite observations for sea level (Sections 3.7 and 13.2), sea surface salinity (Section 3.3), sea ice (Section 4.2) and ocean colour have also been further developed over the past few years.
Progress has also been made with regard to observation of terrestrial Essential Climate Variables. Major advances have been achieved in remote sensing of soil moisture due to the launch of the Soil Moisture and Oceanic Salinity mission in 2009 but also due to new retrieval techniques that have been applied to data from earlier and ongoing missions (see Seneviratne et al., 2010 for a detailed review). However, these measurements have limitations. For example, the methods fail under dense vegetation and they are restricted to the surface soil. Updated Advanced Very High Resolution Radiometer-based Normalized Differenced Vegetation Index data provide new information on the change in vegetation. During the International Polar Year 2007–2009 the number of borehole sites was significantly increased and therefore allows a better monitoring of the large-scale permafrost features (see Section 4.7).
- GCOS, 2009: Progress Report on the Implementation of the Global Observing System for Climate in Support of the UNFCCC 2004–2008, GCOS-129 (WMO/TD-No. 1489; GOOS-173; GTOS-70) , Geneva, Switzerland.
- Brunet, M., and P. Jones, 2011: Data rescue initiatives: Bringing historical climate data into the 21st century. Clim. Res., 47, 29–40.
- Brönnimann, S., T. Ewen, J. Luterbacher, H. F. Diaz, R. S. Stolarski, and U. Neu, 2008: A focus on climate during the past 100 years. In: Climate Variability and Extremes during the Past 100 Years [S. Brönnimann, J. Luterbacher, T. Ewen, H. F. Diaz, R. S. Stolarski and U. Neu (eds.)]. Springer Science+Business Media, Heidelberg, Germany and New York, NY, USA, pp. 1–25.
- Menne, M. J., I. Durre, R. S. Vose, B. E. Gleason, and T. G. Houston, 2012: An overview of the Global Historical Climatology Network-Daily Database. J. Atmos. Ocean. Technol., 29, 897–910.
- Dee, D. P., et al., 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Q. J. R. Meteorol. Soc., 137, 553–597.
- GCOS, 2011: Systematic Observation Requirements for Satellite-based Products for Climate Supplemental details to the satellite-based component of the Implementation Plan for the Global Observing System for Climate in Support of the UNFCCC – 2011 Update, (GCOS-154) – December 2011, Geneva, Switzerland.
- Willis, J. K., J. M. Lyman, G. C. Johnson, and J. Gilson, 2009: In situ data biases and recent ocean heat content variability. J. Atmos. Ocean. Technol., 26, 846–852.
- Seneviratne, S. I., et al., 2012: Chapter 3: Changes in climate extremes and their Impacts on the Natural Physical Environment. In: SREX: Special Report on Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation [C. B. Field, et al. (eds.]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp.109–230.
ES 1.1 1.2.1 1.2.2 1.2.3 1.3 1.3.1 1.3.2 1.3.3 1.3.4 184.108.40.206 220.127.116.11 18.104.22.168 1.4.1 1.4.2 1.4.3 1.4.4 1.5 1.5.1 1.5.2 1.6 Box 1 FAQ Refs