Climate Outlook
Climate Outlook for May-June-July
Background

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.

Outlook

Global

As per recently released 'Global Summary Information' by the US National Oceanic and Atmospheric Administration (NOAA), the globally averaged temperature over land and ocean surfaces for March 2015 was the highest for the month since record keeping began in 1880 (http://www.ncdc.noaa.gov/sotc/global/2015/3).

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 May-June-July 2015 (Fig.1). Strongest anomalies will prevail over western North America, the eastern North Pacific, central and eastern equatorial Pacific. Positive temperature anomalies are also highly probable over the Indian Ocean and maritime continent. Negative temperature anomalies are being expected in the Tibetan plateau region, northern and tropical North Atlantic and central subtropical North Pacific. Positive rainfall anomalies are expected in the central and eastern equatorial Pacific, while negative anomalies prevailing over the maritime continent and Central America. A high probability of below-normal rainfall is predicted across the Maritime Continent. A slightly high probability of below-normal rainfall is predicted over South Asia and the Indochina Peninsula. A high probability of above-normal rainfall around the equatorial date line is predicted.

ENSO

During March through early-April 2015 the Sea Surface Temperatures in the central and eastern parts of Pacific Ocean met the threshold for weak Nino conditions. Most of the atmospheric variables indicate an El Nino pattern, including weakened trade winds, low Southern Oscillation Index and excess rainfall in the vicinity of the dateline. Positive equatorial sea surface temperature (SST) anomalies continue across most of the Pacific Ocean. The consensus of ENSO prediction models indicates that there is an approximately 70% chance that El Nino conditions will continue through Northern Hemisphere summer 2015, and a greater than 60% chance it will last through autumn.

Regional

Probabilistic MME forecasts (based on 11 models) from WMOLC show that May 2015 temperatures are likely to be normal to above normal across the GCC region and with a probability of (40-60%) above average across Qatar. Forecasts also indicate that probability of temperatures to be above normal (60-70% probability) for the season (May-June-July) (Fig. 2). Probabilistic MME seasonal forecast (MJJ) from IRI also shows that the temperature is expected to be above normal (>60% 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 slightly enhanced (25-40%) over Middle East and GCC countries (http://iri.columbia.edu/our-expertise/climate/forecasts/seasonal-climate-forecasts).

The deterministic MME forecasts (based on 12 models from WMOLC) indicate that above normal temperature conditions will prevail in the GCC countries in May 2015. The temperature anomalies for the State of Qatar are expected to be in the range of 0.25°C to 0.5°C for May 2015 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 May as well as season as a whole (MJJ) 2015 over Qatar and adjoining regions.

Climate for State of Qatar in April 2015

The monthly mean, minimum and maximum temperatures recorded in Doha in the month of April are 28.6, 23.9 and 34.6°C respectively. These deviate by +2.0, +2.0 and +1.8°C from their respective long-term Climatological values.

Fig 1. Deterministic 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

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