A climate change monitoring system integrates satellite observations, ground-based data and forecast models to monitor and forecast changes in the weather and climate. A historical record of spot measurements is built up over time, which provides the data to enable statistical analysis and the identification of mean values, trends and variations. The better the information available, the more the climate can be understood and the more accurately future conditions can be assessed, at the local, regional, national and global level. This has become particularly important in the context of climate change, as climate variability increases and historical patterns shift.
Systematic observation of the climate system is usually carried out by national meteorological centres and other specialised bodies. They take measurements and make observations at standard preset times and places, monitoring atmosphere, ocean and terrestrial systems. Since national monitoring systems all form part of a global network, it is vital that there is as much consistency as possible in the way measurements and observations are made. This includes accuracy, the variables measured and the units they are measured in. The World Meteorological Organisation (WMO) performs a vital role in this respect. The National Meteorological or Hydrometeorological Services (NMHS) of 189 member states and territories form the membership of the WMO. This enables the WMO to establish and promote best practice in national climate monitoring, provide support to the NMHSs and effectively implement specific initiatives.
In 1992 the Global Climate Observing System (GCOS) was established to ensure that the observations and information needed to address climate-related issues are obtained and made available to all potential users. The initiative was co-sponsored by the WMO, the Intergovernmental Oceanographic Commission (IOC) of UNSECO, the United Nations Environment Programme (UNEP) and the International Council for Science (ICSU). The stated goal of GCOS is: “to provide comprehensive information on the total climate system, involving a multidisciplinary range of physical, chemical and biological properties, and atmospheric, oceanic, hydrological, cryospheric and terrestrial processes. GCOS is intended to meet the full range of national and international requirements for climate and climate-related observations. As a system of climate-relevant observing systems, it constitutes, in aggregate, the climate observing component of the Global Earth Observation System of Systems (GEOSS)” (http://www.wmo.int/pages/prog/gcos/index.php?name=AboutGCOS, consulted March 2011).
As part of its role to provide vital and continuous support to the United Nations Framework Convention on Climate Change (UNFCCC), GCOS has established 20 Climate Monitoring Principles, as well as defining 50 Essential Climate Variables (ECVs) (http://www.wmo.int/pages/prog/gcos/index.php?name=ClimateMonitoringPrinciples, consulted March 2011). Table 1 below shows the different types of ECV.
|Domain||GCOS Essential Climate Variables|
|Atmospheric (over land, sea and ice)|
SurfaceA: Air temperature, wind speed and direction, water vapour, pressure, precipitation (rain/snow), surface radiation budget
Upper airB: Temperature, wind speed and direction, water vapour, cloud properties, earth radiation budget (including solar radiance)
Composition: Carbon dioxide, methane, and other long-lived greenhouse gasesC, ozone and aerosols, supported by their precursorsD.
SurfaceE: Sea-surface temperature, sea-surface salinity, sea level, sea state, sea ice, surface content, ocean colour, Carbon dioxide partial pressure, ocean acidity, Phytoplankton
Sub-surface: Temperature, salinity, current, nutrients, carbon dioxide partial pressure, ocean acidity, oxygen tracers
River discharge, water use, groundwater, lakes, snow cover, glaciers and ice caps, ice sheets, permafrost, albedo, land cover (including vegetation type), fraction of absorbed photosynthetically active radiation (FAPAR), leaf area index (LAI), above-ground biomass, soil carbon, fire disturbance, soil moisture.
- [A] Including measurements at standardised, but globally varying heights in close proximity to the surface.
- [B] Up to the stratopause.
- [C] Including nitrous oxide (N2O), chlorofluorocarbons (CFCs), hydrochlorofluorocarbons (HCFCs), hydrofluorocarbons (HFCs), sulphur hexafluoride (SF6), and perfluorocarbons (PFCs).
- [D] In particular nitrogen dioxide (NO2), sulphur dioxide (SO2), formaldehyde (HCHO) and carbon monoxide (CO).
- [E] Including measurements within the surface mixed layer, usually within the upper 15m.
Surface atmospheric conditions are the most straightforward of the ECVs to measure. Accurate measurements can be taken using relatively simple equipment. The following instruments are used to measure the different atmospheric surface variables over land, sea and ice:
- A thermometer for measuring air and sea surface temperature
- A barometer for measuring barometric pressure/air pressure
- A hygrometer for measuring humidity
- An anemometer for measuring wind speed
- A wind vane for measuring wind direction
- A rain gauge for measuring precipitation
- A pyranometer for measuring solar radiation
These instruments are usually placed together at a weather station at specific locations on the Earth’s surface. At sea, dedicated weather buoys are equipped with additional instruments to measure the oceanic ECVs.
The global network provided by the WMO enables the national climate monitoring systems of all the member states to feed data into a central database that is accessible to all. This is a vital resource, particularly for developing countries that would not otherwise have access to data collected using state of the art climate monitoring technology. However, the network also creates a responsibility for all member states to ensure that the data they contribute is of sufficient quality. In general there is a need to improve observations at all levels to enhance countries’ ability to adapt to climate change. Effective adaptation planning requires improved observations; improved local, regional, national and global data, as well as denser networks; the recovery of historical data; building of support among the user communities that have a demand for climate information; and promoting greater collaboration between the providers and users of climate information. Working with local populations to incorporate traditional forecasting methodologies can provide key insights into local climate conditions and vulnerabilities that will be essential for effective adaptation planning (Box 1).
Box 1: Traditional Forecasting with Bio-indicators
Although a traditional practice, forecasting with ancestral bio-indicators can be considered an adaptation technology because studies show that they are complementary to climate predictions issued by national meteorological services (Alvarez and Vilca, 2008). In many cases, bio-indicators are more effective for local-level response and adaptation strategies as they provide a more immediate diagnosis than meteorological warnings issues by centralised state entities and are also more adapted to predicting conditions at the local level (Alvarez and Vilca, 2008).
Developed over years of observation and experience, bio-indicators form an essential part of community strategies for disaster risk reduction and climate change adaptation. In terms of development benefits, ancestral bio-indicators enable farmers to maintain productive farming practices and even to take advantage of changes in climate where this leads to longer periods of suitable weather for crop cultivation, or where they are able to adjust crop type to benefit from new climate conditions. Traditional forecasting methodologies incorporate local observations of climatic and other environmental changes or bio-indicators into social organisation to provide an early warning mechanism for hydro-meteorological phenomena that appear suddenly or over time. Environmental bio-indicators of climate change include changes in the behaviour of animals (for example migration and mating seasons), of plants (such as changes in hydrological tolerance, flowing periods and changes in ecosystem composition) and of weather conditions (such as longer, drier periods, increased frequency of cold periods).
Biological indicators are the subject of scientific research, with studies being conducted into organisms including fish, insects, algae, plants and birds and their role as a form of early detection of El Niño Southern Oscillation (ENSO) events (Guralnick, 2002). Rural farmers have learned to observe local bio-indicators as a basis for making strategic decisions about their agricultural production. One such strategy is the observation of certain bio-indicators several months before sowing and during the crop growth cycle in order to make weather forecasts and predictions and adjust planting and cultivation activities accordingly.
According to Hambly and Onweng (1996), in the Kwale District in Kenya, the end of seasons can be predicted by migration of a specific type of monkey, movement of butterflies and budding of some trees. All of these alert the community to prepare the land. The start of the rains is predicted by the change in winds flowing towards the North, the changes in the position of stars and information given by fishermen on the ‘mixing’(inversion of sea water).
In Zimbabwe, interviews with community members captured information on how certain types of trees, birds and some patterns of animal behaviour have, for many years, been used by the Shona people as measures or signals of changes in the quality of their environment. These include: trees as soil fertility indicators, birds as heralds of the rainy season, trees as water level indicators, and abundance of wild fruits as indicators of good rainy season. This approach promotes the active participation of community meteorological observers who keep daily records of local bio-indicators and climate variables captured by basic weather stations installed on their farms. They screen the information, hand over the data to system operators for processing and validation, produce and disseminate weather forecasts and provide guidance and advice (such as on the type of crops or the farming schedule) to their communities in the native language. This model promotes decentralised participatory data collection and monitoring processes which can empower communities to make collective decisions about their livelihood strategies.
The estimated cost of the implementation of a decentralised climate monitoring system that incorporates traditional knowledge in a micro watershed covering 10 local governments is US$ 50,000. Annual operating costs are estimated at US$ 25,000 (Damman, 2008). The limitations of the technology are related to potential scale of application which can usually only occur at a very local level. In addition, in some contexts, increased climate variability can throw into question the validity of biological indicators.
For countries to understand their local climate better and thus be able to develop scenarios for climate change, they must have adequate operational systematic observing networks, and access to the data available from other global and regional networks. These systems enable the integration of national early warning systems, GIS mapping of vulnerable areas, meteorological information on flooding and droughts, as well as the mapping of disease outbreaks. In this way, they provide indicators for monitoring the impacts of climate change and facilitate disaster preparedness and adaptation planning.
The Food and Agriculture Organisation of the United Nations (FAO) is running a number of initiatives aimed at modelling the impacts of climate change on agriculture, which provide vital information for national decision-making and planning (Box 2).
Box 2: FAO Climate Change and Agriculture Modelling
Source: the FAO website www.fao.org/nr/climpag; Kuika et al, 2011
There are many advantages of having a comprehensive and reliable national climate monitoring system. On a national level, accurate weather forecasting is invaluable for many sectors, particularly agriculture. In developing countries, where the main economic activity of a majority of the population is linked to agriculture, predictions about what environmental conditions can be expected during the year can have a huge impact on people’s livelihoods and the national food supply. Decisions about what crops to plant, when to plant and when to harvest are crucial and the more accurately weather can be forecasted, the better decisions can be taken (Box 3).
Box 3: Agricultural Climate Risk Zoning in Brasil
Since 1996, the Brazilian Ministry of Agriculture and EMBRAPA (Brazilian Enterprise for Agricultural Research) has coordinated the Agricultural Zoning Programme with the goal of increasing agricultural productivity by reducing agricultural losses due to incorrect sowing periods. In the State of São Paulo sowing periods for rice, beans, maize, soybean and wheat have been defined to minimise impacts from dry periods and high temperatures during the reproductive phase, very humid periods during harvest, and low temperatures during the cropping cycle. The planting periods were defined through the simulation of a climatic water balance that gives an index of water supply (ISNA) using historical rainfall data, potential evapo-transpiration, physiological characteristics of each crop, and water retention by the soil. The following results can be highlighted:
Source: Zullo Jr et al, 1999
One of the effects of climate change seems to be the more frequent occurrence of extreme weather events. These include hurricanes and typhoons, as well as unseasonal extremes of temperature and heavy rains, which can cause droughts, flooding, landslides and other disasters. The devastation that these events can suddenly have on agricultural production means that any improvement on the ability to predict or anticipate them and plan accordingly is invaluable. Due to the complexity of global climate and weather systems and the fact that our understanding is based on modelling using historical data, the regular measurements of specific variables provided by climate monitoring systems is essential for developing early warning systems.
The main disadvantage of a national climate monitoring system is the cost. Not just the capital required to purchase, install and/or operate all the necessary equipment, but also the ongoing costs of maintaining the equipment and ensuring accurate collecting of data, building and maintaining databases, making sure that that data is correctly interpreted and, ultimately, ensuring that relevant information is communicated to the appropriate people in a timely fashion. The quality of the information produced by a climate monitoring system is only as good as the quality of the data collected. Inaccurate data resulting from malfunctioning equipment, or gaps in coverage caused by lack of equipment, distort results and can lead to erroneous forecasting.
For many developing countries, insufficient resources are allocated to building and maintaining a national climate monitoring system. Also, due to the numerous pressing problems confronting many developing countries, there has often been a tendency for governments and policy makers to focus on short-term solutions to problems. In the case of most African countries, for example, climate is not systematically integrated into longer-term planning and investment decision-making.
Financial requirements for establishing or improving a national climate monitoring system are considerable. The GCOS Regional Action Plans for ten developing regions of the world detail priority needs for improvements in atmospheric, oceanic, and terrestrial observing systems totalling more than US$ 200 million. Common needs include sustaining and improving operational observing networks; recovering historical data; and education, training, and capacity building. To boost weather and climate monitoring systems in Africa, the African Development Bank (AfDB) and the World Bank have agreed to provide 155 million dollars through the African Centre of Meteorology Applications for Development. In Cameroon, a National Observatory on Climate Change has been set up with US$ 6 million of funding. The observatory is aimed at providing climate data monitoring the effects of climate change on the country’s people, agriculture and ecosystems, and guiding work on climate action.
A national climate monitoring system is itself a network of regional and local monitoring resources, but the whole system must be managed and coordinated by the designated National Meteorological or Hydrometeorological Service (NMHS). The NHMS should also share climatic data readily with other relevant national and international organisations, as well as with researchers.
The principal barriers to implementation are the financial and human resources required to set up and maintain a national monitoring system. The hardware, software and trained personnel needed are a big financial and time commitment. For many developing countries, other more pressing problems have a greater call on these resources.
Part of the GCOS initiative is the GCOS Cooperation Mechanism (GCM). The GCM aims to develop a coordinated multi-governmental approach to address the high-priority needs for stable, long-term funding for key elements of global climate observing systems, especially those in developing countries. The GCM Donor Board has established appropriate procedures for developing funding proposals, manages the allocation of funds, monitors implementation activities and liaises with relevant national and international institutions and mechanisms. Features of the GCM funding mechanism include:
- Development of a critical mass of funding to support achievement of sustained improvements in global observing systems for climate
- Capability to address all types of funding requirements for global climate observations in developing countries, including system improvement, sustained operations, and capacity building
Ability to develop, fund and implement cross-cutting approaches relevant to all climate disciplines/regimes, including addressing data management and data exchange.
Alvarez, G. S. and L. T. Vilca (2008) Ancestral Bio-Indicators in Andean Highland Regions: Disaster Warning and Resilience Mechanisms, Mountain Forum Bulletin 8 (2).
Damman, G. (Ed.) (2008) Sistemas de información y alerta temprana para enfrentar al cambio climático: propuesta de adaptación tecnológica en respuesta al cambio climático en Piura, Apurímac y Cajamarca, Soluciones Prácticas-ITDG, Lima, Peru. P.166.
Guralnick, J. (2002) Biological Indicators as Early Warning of ESNO Events. Regional Disaster Information Centre (CRID).
Hambly, H.V. and T. Onweng Angura (eds) (1996) Grassroots Indicators for Desertification: Experience and Perspectives from Eastern and Southern Africa. IDRC, Ottowa, 1996.
Kuika, O., F. Reynèsa, F. Delobelb, and M. Bernardib (2011) FAO-MOSAICC: The FAO Modelling System for Agricultural Impacts of Climate Change to Support Decision-making in Adaptation, Food and Agriculture Organization of the United Nations, Rome, Italy14-Apr-11, available at: https://www.gtap.agecon.purdue.edu/resources/download/5306.pdf.
Zullo Jr, J., H. Silveira Pinto and D. Assad (2006) Impact assessment study of climate change on agricultural zoning, Meteorological Applications, Supplement: Weather, Climate and Farmers 13 (S1), 69–80.