This technology allows for a forecast of weather conditions for a period of three to six months ahead. Seasonal forecasts are based on existing climate data; in particular, on sea surface temperatures, which are then used in ocean-atmosphere dynamic models, coupled with the synthesis of physically plausible national and international models (Oxford Dictionary of Geography. A Dictionary of Geography. Copyright © Susan Mayhew 1992, 1997, 2004). Seasonal forecasts can be developed using mathematical models of the climate system (Alexandrov, 2006).
According to the World Meteorological Organisation (WMO) definitions, Seasonal to Interannual Prediction (SIP) ranges from 30 days up to two years: monthly outlook, three-month outlook (description of averaged weather parameters expressed as a departure from climate values for that 90-day period) and seasonal outlook (WMO, 2010).
Modern and science-based systems facilitate seasonal forecasting. Predicting climate seasonal anomalies requires the use of complex coupled atmosphere-ocean models. It is believed that ocean variability is an important factor influencing climate variations and changes due to the ocean’s larger capacity to absorb from and release heat back into the atmosphere. A considerable effort has been made to improve the understanding of the phenomena responsible for seasonal variability and most of the major meteorological institutions around the world have developed Ensemble Prediction Systems (EPS) for operational seasonal forecasting based on coupled atmosphere-ocean general circulation models (Grupo de Meteorología de Santander, 2010).
The following are the officially designated WMO Global Producing Centres (GPCs) of Long Range Forecasts: Bureau of Meteorology (BoM) from Australia, China Meteorological Administration (CMA)/Bejing Climate Centre (BCC), Climate Prediction Centre (CPC) NOAA from USA, European Centre for Medium-Range Weather Forecasts (ECMWF), Japan Meteorological Agency (JMA)/Tokyo Climate Centre (TCC), Korea Meteorological Administration (KMA), Météo-France, Met Office from United Kingdom, Meteorological Service of Canada (MSC), South African Weather Services (SAWS), Hydrometeorological Centre of Russia. WMO has also designated the following Lead Centres: WMO Lead Centre for Long Range Forecast Multi-Model Ensemble (LC-LRFMME) jointly coordinated by KMA and NOAA/NCEP, WMO Lead Centre for Standard Verification System of Long Range Forecasts (LC-SVSLRF) jointly coordinated by BoM and MSC. Other leading centres providing global seasonal forecasts are the Centre for Weather Forecasts and Climate Studies/National Institute for Space Research (CPTEC/INPE) from Brazil, the International Research Institute for Climate and Society (IRI) from USA and APEC (Asia-Pacific Economic Cooperation) Climate Centre (APCC) from the Republic of Korea.
Climate change is challenging traditional knowledge about seasonal forecasting and farmers can no longer predict climate using natural indicators. According to Troccoli et al (2007), “(farmers) often have traditional ‘seasonal forecasting’ methods based on bird, animal and plant observations. However, while traditional practices may be resistant to change, experience often demonstrates farmers’ desires for ‘other’ knowledge systems that may be used alongside, and perhaps ultimately may displace, local practices” (Troccoli et al, 2007, 303). For instance, farmers in Burkina Faso have always used winter temperatures, the date and quantity of the first rains, and the special forecasting knowledge of diviners and religious leaders. However, they have admitted that traditional indicators are no longer working due to changes in the climate and so they welcome new information (Kirshen et al, 2003).
Although knowledge and understanding of the socio-economic circumstances is important and must be taken into account, Meinke and Stone (2005; 221) have demonstrated how knowledge of climatic variability can lead to better decisions in agriculture, regardless of geographical location and socio-economic conditions. Within agricultural systems, this technology can increase preparedness and lead to better social, economic and environmental outcomes. It helps decision-making, from tactical crop management options, commodity marketing to policy decisions about future land use (idem).
According to their research, and based on a range of temporal and spatial scales, the types of agricultural decisions that could benefit from targeted climate forecasts are listed in Table 1.
Table 1: Agricultural decisions and climate forecasts
|Example of decision types||Frequency (years)|
|Logistics (e.g., scheduling of planting/harvest operations)||Intra-seasonal (<0.2)|
|Tactical crop management (e.g., fertiliser/pesticide use)||Intra-seasonal (0.2-0.5)|
|Crop type (e.g., wheat or chickpeas) or herd management||Seasonal (0.5-1.0)|
|Crop sequence (e.g., long or short fallows) or stocking rates||Inter-annual (0.5-2.0)|
|Crop rotations (e.g., winter or summer crops)||Annual/bi-annual (1-2)|
|Crop industry (e.g., grain or cotton; native or improved pastures)||Decadal (~10)|
|Agriculture industry (e.g., crops or pastures)||Inter-decadal (10-20)|
|Land use (e.g., agriculture or natural systems)||Multi-decadal (>20)|
|Land use and adaptation of current systems||Climate change|
Source: Meinke and Stone 2005
Moreover, SIP is linked to a great variety of practical applications, from security related issues, such as water resource management, food security, and disaster forecasts and prevention; to health planning, agriculture management, energy supply and tourism. It is an important element in some policy/decision-making systems and is key to achieving the longer-term goals of climate change adaptation strategy (Troccoli et al, 2007). In Eastern Europe for instance, SIP is taken into consideration for the strengthening of drought preparedness and management, including drought contingency plans, at the local, national, sub-regional and regional levels (Alexandrov, 2006).
When considering the limitations of this technology, it is worth mentioning that despite important achievements relating to adaptation strategies based on seasonal forecasting systems, significant levels of skill are generally only found in regions strongly connected with the El Niño Southern Oscillation (ENSO) (Arribas et al, 2009). This is a quasi-periodic, inter-annual variation in global atmospheric and oceanic circulation patterns that causes local, seasonal rainfall to vary at many locations throughout the world (Meinke and Stone, 2005; 228). In fact, ENSO forecasting is the main example of seasonal climate prediction which is why there is continuous improvement in the techniques involved. For example, the Met Office in the UK has developed a new seasonal forecasting system (GloSea4) that is flexible, easy to upgrade and enables improved forecasting over the El Niño regions (http://www.metoffice.gov.uk/research/modelling-systems/unified-model/climate-models/glosea4).
To implement this technology it is necessary to establish a meteorological service with skilled, trained and experienced personnel. This implies high costs if a country or region is starting from scratch, although these costs could be substantially reduced by using offices in public buildings and by partnering with scientific institutes and Global Producing Centres.
To use this tool effectively, Meinke and Stone suggest a participatory, cross-disciplinary research approach that brings together institutions (partnerships), disciplines (such as climate science, agricultural systems science, rural sociology, and many other disciplines) and people (scientists, policy makers and direct beneficiaries) as equal partners: “climate science can provide insights into climatic processes, agricultural systems science can translate these insights into management options and rural sociology can help determine the options that are most feasible or desirable from a socio-economic perspective” (2005, 221).
The interpretation of the seasonal predictions of climate are not easy for most agricultural technicians and farmers to interpret as they are given as probabilities of positive or negative variations in temperature or precipitation. Although it must be recognised that all such predictions have an uncertainty associated with them, agricultural stakeholders need a lot of assistance as to how to identify the likely seasonal trends. Equally, meteorological services need staff with skills to present the information in a way that the public can interpret and make use of it.
Access to forecasting (weather and seasonal) and climate information is common across most adaptation contexts. However, as with other interfaces between communities and experts, it will require investment in appropriate methods of communication and knowledge exchange (Ensor and Berger 2009) such as targeted campaigns to promote the information usage and e-platforms promoted in local communities.
Making seasonal forecasting relevant to small-scale farmers and making sure the information reaches them represent the main challenges. For this reason, communication strategies are the key to using this technology effectively. Based on her experience in Lesotho, Ziervogel has pointed out that although seasonal climate forecast information is useful to some farmers, disseminating the information is a challenge. This is because it is often disseminated in English rather than Sesotho and via a press release that does not have the follow-up support that farmers would like. As a result, they are unable to examine the information in greater depth. This hampers discussion between farmers and experts as to what are the information needs and how it might be used (Ziervogel, 2007).
Kirshen et al (2003;4) have pointed to some specific communication challenges that need to be taken into account, based on lessons learned from climate change adaptation experience in West Africa:
- Distribution: there is not always equitable distribution of the forecasts to different village groups
- Measurements: farmers think in terms of crop production, livestock health, and water availability, not rain quantity
- Concepts: it is important to explain that a forecast is based on probabilities, not certainties and that it covers a specific region or area
- Media: most farmers can be reached by traditional media but they might have specific questions that need to be answered directly. The Climate Forecasting for Agricultural Resources (CFAR) project has run workshops in which ‘key’ farmers (i.e. those who interact a lot with other famers) explain forecasts. These farmers then act as intermediaries to spread the forecast to other farmers in their villages. This Project is of Tufts University and the University of Georgia funded by the Human Dimensions of Global Change Program, National Oceanic and Atmospheric Administration
Complementary approaches suggest that instead of replacing traditional farmers’ forecasting, adaptation will be made easier if new forecasts are treated synergistically alongside traditional methods as a sympathetic way to introduce the use of new technologies (Troccoli et al, 2007; 303).
As with most part of technologies applied at a national level, opportunities for implementation can be found where there is strong political will of implementing a national action plan to cope with climate change because of the type of investment required, and where communities work in vertical networks (with government and formal institutions).
Alexandrov, V. (2006) Using Better Climate Prediction in the Implementation of National Action Programmes (NAPs) – (Eastern) Europe, Environmental Science and Engineering, 537-551.
Arribas, A., M. Glover, A. Maidens, K. A. Peterson, M. Gordon and C. MacLachlan (2009) Towards a new generation of seasonal forecasting systems. Física de la Tierra, 21, 219-224, UK.
Ensor, J. and R. Berger (2009) Understanding Climate Change Adaptation: Lessons from community-based approaches, Practical Action Publishing, Rugby, UK.
Kirshen, P., K. Ingram, G. Hoogenboom, C. Jost, C. Roncoli, M. Ruth and K. Knee (2003) Lessons Learned for Climate Change Adaptation; Part 1 – Implementation of seasonal climate forecasting in West Africa; Part 2- Impacts from and adaptation to climate change in Metro Boston,USA. Prepared for Insights and Tools for Adaptation: Learning from Climate Variability, 18-20. November 2003, Washington, DC
Meinke, H. and R. C. Stone (2005) Seasonal And Inter-Annual Climate Forecasting: The New Tool For Increasing Preparedness To Climate Variability And Change In Agricultural Planning And Operations. Climatic Change 70: 221–253
Troccoli, A., M. Harrison, D. L. T. Anderson, S. J. Mason (eds.) (2007) Seasonal Climate: Forecasting and Managing Risk Springer Academic Publishers, Dordrecht / Boston / London.
Ziervogel (2001) Global science, local problems: Seasonal climate forecast use in a Basotho village, southern Africa, Gina Ziervogel, Environmental Change Institute. Paper prepared for presentation at the Open Meeting of the Global Environmental Change Research Community, Rio de Janeiro, October 6-8.
Selective breeding via controlled mating
Climate Change Monitoring System
Seed and Grain Storage
Seed and Grain Storage
Seed and grain storage
Water user groups
Decentralised Community-run Early Warning Systems
Household water treatment and safe storage
Ecological Pest Management
Floating agricultural systems