The development of some ability for forecasting low rainfalls would be helpful in Tuvalu as rainwater is the only source of fresh water in the country. The subsurface water is brackish and saline so the entire country depends totally on rainwater for daily domestic supplies, agricultural and farming activities. More importantly, these atolls are often influenced by droughts which consequently make inadequate drinking water an issue. A simple graph-based forecasting scheme is developed and presented in this thesis for forecasting below average mean rainfall in Funafuti over the next n-month period. The approach uses precursor ocean surface temperature data to make predictions of below average rainfall for n = 1, 2 12. The simplicity of the approach makes it a suitable method for the country and thus for the Tuvalu Meteorological Service to use as an operational forecasting tool in the climate forecasting desk. The graphical method was derived from standardised monthly rainfalls from the Funafuti manual raingauge for the period January 1945 to July 2007. The method uses lag-1 and-lag 2 NINO4 sea surface temperatures to define whether prediction conditions hold. The persistence of predictability tends to be maintained when the observed NINO4 ocean surface temperatures fall below 26.0oC. Although the developed method has a high success probability of up to 80 percent, this can only be achieved when conditions are within the predictable field. A considerable number of below average rainfall periods are not within the predictable field and therefore cannot be forecast by this method. However, the graphical approach has particular value in warning when an existing drought is likely to continue.

Långtidsprognoser beskriver gapet mellan väderprognoser och klimatmodeller som förutspår klimatförändringar p.g.a. den globala uppvärmningen. Långtidsprognostik förlitar sig på så kallade “källor av förutsägbarhet”. Dessa kan vara variabler som ändrar sig långsamt, som havsvattnets yttemperatur, eller variationsmönster såsom El Nino-Southern Oscillation. Många industrisektorer och delar av samhället som ´ idag använder väderprognoser i sina dagliga beslut, som t.ex. jordbruksindustrin, energiindustrin eller någon annan väderkänslig sektor, kan potentiellt dra nytta av träffsäkra och pålitliga långtidsprognoser. Bland de potentiella användarna finns förstås också privatpersoner. Syftet med denna studie är att introducera långtidsprognostik och att försöka utvärdera prestandan av ECMWFs välkända och etablerade säsongsmodell SEAS5, genom tidigare forskning och ECMWFs verifikationsfigurer som finns tillgängliga för allmänheten. Utvärderingen gjordes utifrån allmänhetens perspektiv och vad de skulle uppfatta som en “bra” prognos. Marknära temperatur och delvis nederbörd undersöktes för Europa under sommar och vinter, i termer av “skill” (träffsäkerhet) och “reliability” (pålitlighet, på så sätt att en händelse som förutspås med 60% sannolikhet också ska observeras i ungefär 60% av fallen). SEAS5 når inte riktigt upp i “bra” skill-nivåer för marknära temperatur, om “bra” motsvarar synoptiskt användbar prognos. Det finns dock stora skillnader mellan olika platser och säsonger, där “bra” skill framförallt märks i södra/sydöstra Europa på sommaren och delar av norra Europa på vintern. “Reliability” är generellt bra, även om det är svårt att avgöra hur den skiljer mellan olika platser. Nederbörd visar dock mycket dålig skill och låg “reliability” oavsett säsong eller plats. / Long range weather prediction describe weather forecasts with a range longer than 14 days, but shorter than climate prediction models that predict climate change due to global warming. Long range forecasting relies on sources of predictability that, for example, changes slowly such as sea surface temperatures, or varies in predictable patterns like the El Nino-Southern Oscillation. Many sectors of industry and society ´ that today use weather forecasting in their day-to-day decision making, such as agriculture, energy or any other weather sensitive sector, have potentially much to benefit from accurate and reliable long range forecasts. Among potential users is of course the general public. The purpose of this study is to give an introduction to long range weather prediction and attempt to evaluate the performance of ECMWF’s SEAS5 seasonal model, which is one of the most well known and established S2S models, by using earlier research and ECMWF’s publicly available verification charts. This was done from the public’s perspective of what would be considered a “good” forecast, mainly from near surface air temperature but also precipitation, during winter and summer in Europe, on the aspects of skill (accuracy) and reliability (in the sense that a probabilistic forecast of 60% for an event also is observed around 60% of the time). SEAS5 overall doesn’t quite reach “good” skill levels for near surface temperature, if “good” is defined as synoptically significant. The skill level varies significantly though, depending on region and season, with southern/southeastern Europe during summer and parts of northern Europe during winter being notable “good” situations. Reliability is generally good, even if it is difficult to know how reliability varies spatially. However, precipitation shows very little skill and low reliability, no matter the season or region.