Inter-annual variations can affect global and regional atmospheric and oceanic circulation. Many of these variations are recurrent and are usually depicted with well known climatic patterns such as the El Niño Southern Oscillation (ENSO), the North Atlantic Oscillation (NAO), etc. They correlate significantly with the departures from the mean state of climate parameters at monthly, seasonal and annual time scales and with the onset of extreme weather and climate events leading to direct and indirect consequences on lives, goods, properties and the well being of societies. Droughts, heat waves, cold waves, flooding, extreme wind storms, land slides, bush and forest fires, costal erosions to list just these are the most popular induced impacts which may be triggered by one or several of such anomalies. In the context of global warming these extremes are expected to become in the future more frequent, more severe and gaining more geographical extend than usually known. Some of the observed increase in climate extremes already fit in these projections.
To this effect, National Meteorological and Hydrological Services (NMHSs) should be adequately equipped and prepared to continuously monitor and assess the state of the climate, evaluate available long range forecasts, and where conditions warrant provide to the users concise and understandable climate early warning information at weekly, 10-day, monthly, and seasonal time scale. A climate advisory as an output of a Climate Watch System has the following characteristics:
- Issued to heighten awareness in the user community concerning a particular state of the climate system;
- Disseminated to serve as a mechanism for initiating preparedness activities by users and/or a series of events that affect user decision making;
- Based on real-time monitoring (current status) of conditions and on climate outlooks;
- Issued by individual NMHSs, perhaps in coordination with other NMHSs or regional Climate Centers in the region or beyond;
- Developed as a result of continuous and iterative collaboration with users