Climate Outlook for January-February-March 2016


The Qatar Meteorological Department is introducing monthly and seasonal (3-month) climate outlooks for Qatar and adjoining regions, beginning April 2014. Monthly and seasonal climate forecasts are generated world wide with an underlying assumption that sea surface temperatures in global oceans, snow-cover, soil-moisture of the land surface and other such slowly varying boundary conditions determine the ensuing global and regional climate anomalies. Leading climate centers around the world use fully coupled ocean-atmosphere general circulation models for generating seasonal climate forecasts wherein both sea surface temperatures and associated atmospheric circulation co-evolve. Climate forecasts are generally in the form of anomalies from a long-term climatology as simulated in the models. Individual climate centers produce monthly/seasonal forecasts based on a large suite of simulations (generally initiated every month and extending 6 months in to the future) with different initial conditions using their global climate model and then develop an ensemble forecast product employing advanced statistical techniques.  Show Details...

World Meteorological Organization (WMO) supports and coordinates seasonal forecasts through lead centers (LC) of long-range forecasts (LRF). Korea Meteorological Administration, Seoul, maintains one such lead center. This WMO Lead Center (WMOLC) uses all available forecasts from different climate centers around the world and generates multi-model ensemble (MME) monthly and seasonal climate forecasts for the globe. Several meteorological variables are predicted which include surface temperature (2m), precipitation etc. Apart from WMOLC, other climate centers like International Research Institute for Climate and Society (IRI, New York, USA), National Centers for Environmental Prediction (NCEP/NOAA, USA), APEC Climate Center (APCC, Korea), UK Meteorological Office (UKMO, UK), ECMWF (UK), Bureau of Meteorology (BoM, Australia) etc. also produce monthly/seasonal forecasts.

In preparation of this Climate Outlook for Qatar the monthly/seasonal products generated by the WMOLC have been used. This approach takes in to account a majority of the model forecasts from the Global Producing Centers (GPCs) and combines them using ensemble mean forecast system. However, we also take in to consideration of other forecast products while generating the outlook for the regional temperature and precipitation anomalies for the coming month and season.

The skill of WMOLC ensemble mean forecast products varies from month to month and from one meteorological variable to the other. Generally the skills of temperature forecasts are larger compared to the precipitation forecasts. This disparity in skills between temperature and precipitation forecasts is not unique to this region and is the case globally. The correlation skills estimated based on retrospective (hindcast) forecasts during the past 30 years of MME prediction system are in the range of 0.4-0.6 during different months in the year and for the surface temperatures over this region. These are considered reasonably high compared to the skills observed in other parts of the world.

The current Climate Outlook for Qatar is mainly for monthly/seasonal mean temperature forecast. The reason for the initial focus on temperatures is both due to higher skill and its prime relevance to climate sensitive activities in the national context. With further review of predictive skill and demand of user sectors, the scope of the seasonal climate outlooks can be expanded to cover more features of relevance to this region such as the probability of high wind events, exceedance of certain temperature thresholds etc.

Two types of forecast products are generated by climate centers. One is the probabilistic forecast where the seasonal climate anomalies are predicted to be in one of the three categories (below normal, normal and above normal) and the probability of predicted values falling in these categories is estimated. The other is the deterministic forecast where the magnitude of anomalies is estimated for the ensuing month and the season using different statistical techniques. This outlook uses the forecasts estimated based on simple ensemble mean after correcting the biases in each of the participating models.



The State of the Climate Summary Information is a synopsis of the collection of national and global summaries released each month. As per recently released 'Global Summary Information – November 2015' by the US National Oceanic and Atmospheric Administration (NOAA), the globally-averaged land surface temperature was 2.36°F (1.31°C) above the 20th century average. This was the fifth highest for November in the 1880–2015 record. (

The World Meteorological Organization Lead Center for Long-Range Forecast (WMOLC LRF) indicates that the positive temperature anomalies will prevail over most of the global land areas for the months January-February-March 2016 (Fig.1). Highly enhanced probability for above normal temperature is predicted for eastern and central tropical Pacific, western and eastern North Pacific, extra tropical North America, Central and tropical South Americas, Africa, the Indian Ocean and Southern Asia. Below normal temperature is highly probable over the southern North America and subtropical South America. Strongly enhanced probability for above normal precipitation is predicted for the central and eastern equatorial Pacific and the Caribbean Sea. Strongly enhanced probability for below normal precipitation is predicted for the north-eastern part of maritime continent and adjacent seas, tropical North and South Pacific surroundings.


El Nino, a state of warmer-than-average sea surface temperature conditions, is present in the tropical Pacific. During mid-December 2015 the tropical Pacific SST was at a strong El Nino level. Positive equatorial sea surface temperature (SST) anomalies continue across most of the Pacific Ocean. All atmospheric variables strongly support the El Nino pattern, including weakened trade winds and excess rainfall in the east-central tropical Pacific. The consensus of ENSO prediction models indicate continuation of strong El Nino conditions during the December-February 2015-16 season in progress. El Nino is expected to remain strong through the Northern Hemisphere winter 2015-16, with a transition to ENSO-neutral anticipated during late spring or early summer 2016.


Probabilistic MME forecasts (based on 11 models) from WMOLC show that January 2016 temperatures are likely to be above normal across the GCC region and with a probability of more than 50% above average across Qatar. Forecasts also indicate that probability of temperatures to be above normal (>50% probability) for the season (January-February-March) (Fig.2). Probabilistic MME seasonal forecast (JFM) from IRI also shows that the temperature is expected to be above normal (>50% probability) in the Middle East and GCC countries along with more than 60% probability of above normal temperatures in the state of Qatar. The probability of occurrence of temperature extremes to be in the category of slightly enhanced to enhanced category (25-50%) over Middle East and GCC countries (

The deterministic MME forecasts (based on 12 models from WMOLC) indicate that above normal temperature conditions will prevail in the GCC countries in January 2016. The temperature anomalies for the State of Qatar are expected to be above normal in the range of 0.50°C to 1.0°C for January 2016 and such above normal conditions are likely to persist in coming two months as well (Fig. 3). Forecasts from other climate centers are generally in agreement with WMOLC outlook for Qatar and the GCC.

Most of the models from WMOLC indicate near normal precipitation during January 2016 and above normal conditions are likely to persist in the season as a whole (JFM) over Qatar and adjoining regions.

Climate for State of Qatar in December 2015

The monthly mean, minimum and maximum temperatures recorded in Doha in the month of December are 20.0, 16.8 and 23.6°C respectively. These deviate by +0.1, +0.7 and -0.8°C from their respective long-term Climatological values.

Fig 1. Probabilistic MME forecast from WMO LC LRF: 2m Temperature and precipitation

Fig 2. Probabilistic MME forecast from WMO LC LRF : 2m Temperature

Fig 3. Deterministic MME forecast from WMO LC LRF : 2m Temperature