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.
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
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
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 - May 2015' by the US National Oceanic and Atmospheric Administration (NOAA), the globally averaged temperature over land and ocean surfaces for May 2015 was 1.57°F (0.87°C) above the 20th century average. This was the highest for May in the 1880–2015 record, surpassing the previous record set last year in 2014 by 0.14°F (0.08°C) (http://www.ncdc.noaa.gov/sotc/summary-info).
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 July-August-September 2015 (Fig.1). Strong positive anomalies are expected over western North America, the eastern North Pacific, the Indian Ocean, central and eastern equatorial Pacific. Warm conditions are also expected in some areas of southern ocean basins. Negative temperature anomalies are predicted for the northern North Atlantic, central tropical and subtropical South Pacific. Strong positive precipitation anomalies associated with developing El-Nino are highly probable in the central and eastern equatorial Pacific, surrounded by a horseshoe of strong negative precipitation anomalies over the Maritime Continent, south Asia, and subtropical North and South Pacific. Above normal precipitation is also expected over North and South Americas. A belt of the strong negative precipitation anomalies is expected to span the tropical Atlantic from Central America through western Africa.
El Nino, a state of warmer-than-average sea surface temperatures in the tropical Pacific, has recently intensified. During late May through early-June 2015 the SST was at a moderate El Nino level. Positive equatorial sea surface temperature (SST) anomalies continue across most of the Pacific Ocean. The atmospheric variables support the El Nino pattern, including weakened trade winds and excess rainfall in the east-central tropical Pacific. The consensus of ENSO prediction models indicates that there is a greater than 90% chance of El Nino and it will continue through Northern Hemisphere fall 2015, and around an 85% chance it will last through the 2015-16 winter.
Probabilistic MME forecasts (based on 11 models) from WMOLC show that July 2015 temperatures are likely to above normal across the GCC region and with a probability of (50 - 70%) above average across Qatar. Forecasts also indicate that probability of temperatures to be above normal (60 - 70% probability) for the season (July-August-September) (Fig. 2). Probabilistic MME seasonal forecast (JAS) from IRI also shows that the temperature is expected to be above normal (>70% probability) in the Middle East and GCC countries along with more than 70% probability of above normal temperatures in the state of Qatar. The probability of occurrence of temperature extremes to be enhanced (40-50%) over Middle East and GCC countries
The deterministic MME forecasts (based on 11 models from WMOLC) indicate that above normal temperature conditions will prevail in the GCC countries in July 2015. The temperature anomalies for the State of Qatar are expected to be normal and it is in the range of -0.25°C to 0.25°C for July 2015 and such 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 July as well as season as a whole (JAS) 2015 over Qatar and adjoining regions.
Climate for State of Qatar in June 2015
The monthly mean, minimum and maximum temperatures recorded in Doha in the month of June are 36.3, 32.0 and 41.3°C respectively. These deviate by +1.7, +2.9 and -0.3°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