Outstanding Issues in Seasonal to Interannual Climate Prediction II Posters
Presiding: M Tippett, International Research Institute for Climate Prediction; D DeWitt, International Research Institute for Climate Prediction
A41A-01 0830h
Looking Past ENSO, What is Causing the Recent Extreme Climate Signals?
Over the past few years, there have been large climate signals not associated with El Nino and the Southern Oscillation (ENSO). Because ENSO is our best source of seasonal predictability, forecasts have done poorly predicting these events. Since the El Nino of 1997/8, western portions of the United States have been experiencing a major drought. The two years after the El Nino, the Pacific Ocean was in a La Nina state, which is typically associated with drought in the South Western United States, but more recently the La Nina conditions have disappeared. Since then a weak El Nino has come and gone, but the drought failed to break. Also, the summer of 2003 was the warmest on record for many parts of Europe. Statistical analysis of historical data suggests that such a large anomaly is extremely unlikely to occur, but alas it did. This study will investigate whether these events are driven by the chaotic nature of the atmosphere, or whether there is a signal in the sea surface temperature (SST) that is potentially predictable. The results to be presented are from the NSIPP-1 AGCM, which is the atmospheric model used in the seasonal forecasts generated by the Global Modeling and Assimilation group at NASA Goddard. The model failed to persist the drought into the 21st Century, nor did it capture the magnitude of the warm anomalies over Europe when forced with observed SSTs. A series of higher resolution runs (0.5 degree) are being carried out to assess the impact the increase of resolution has on the simulation of rainfall and temperature anomalies. Also, idealized experiments will assess the model's sensitivity of SSTs outside of the tropical Pacific Ocean and initial conditions.
A41A-02 0830h
The Impact of Soil Moisture Initialization on Seasonal Precipitation in the West African Sahel Using the Regional Spectral Model
This study investigates the extent to which prior knowledge of soil moisture states can influence seasonal precipitation predictability in the West African Sahel. This region has been characterized as a semi-arid region which is the transition zone that model studies have suggested that the impact of land state initialization on precipitation predictability may be most significant. Results will be presented from (1)an empirical orthogonal function (EOF) analysis of neutral years from 1981-2004 of May/June monthly Reanalysis-2 soil moisture data to identify the leading modes of soil moisture variability in the region and (2) a sensitivity analysis that consists of a seasonal integration (May-September) initialized with soil moisture patterns that are characteristic of the first two leading modes of soil moisture variability.
A41A-03 0830h
Verification of hemispheric-wide winter temperature forecasts based on fall snow cover.
One outstanding issue in seasonal climate prediction is whether there is any robust predictability beyond ENSO dynamics. We have operationally produced real-time winter forecast for the US based on fall Eurasian snow cover for the past six years. Operational forecasts have been expanded to include Europe for the past two years and East Asia this past winter. Here we assess the skill of these forecasts, up through the most recent winter season. Particular successes are shown for the US for the past four winters and for Europe over the past two. These snow-based forecasts appear to provide considerable additional information beyond the standard-ENSO based forecasts.
A41A-04 0830h
Modeling Prairie Wetland Weather and Climate Feedbacks in the Northern Great Plains
Storm-scale simulations of the Northern Great Plains have shown that the prairie wetland systems in the region influence warm-season convective systems even under synoptic-scale forcings. These complex surface water systems, in turn, swell in surface area during wet cycles and contract (and in some cases completely disappear into the cropland/pastureland land cover matrix) during dry cycles. Since the early-to-mid 1990s, these wetland systems have expanded to their historical maximum. The resulting expansions have had an impact on surface hydrology, and agricultural practices (including new crop rotation regimes in the affected areas) and may impact regional climate feedbacks. To examine the potential for these feedbacks, we shall present results of regional climate simulations of the recent decadal period featuring comparisons of precipitation and evaporation patterns with the ambient land cover regimes currently used in mesoscale and regional climate models (which do not include any reference to the larger wetland system in the region), and approximations of the pre-expansion and current wetland states using modified land cover and soil moisture patterns as a proxy. These latter simulations represent a first step in developing a companion wetland parameterization which could facilitate not only coupled hydroclimatological studies of the region, but ecological and biogeochemical studies as well.
A41A-05 0830h
Seasonal and Interannual Variations over the Gulf of Maine: Comparisons of the National Data Buoy Center Observations and Eta Model Results
Results from the NOAA/NCEP Eta 221 AWIPS meteorological model and the North American Regional Reanalysis are compared with the observations taken by buoys and C-MAN stations in the Gulf of Maine to determine the model's ability to predict the weather over this particular body of water. The long time series provided by the buoys has allowed us to examine the interannual variations from the past two decades along with seasonal variations. Overall the model is fairly accurate and there are recognizable seasonal and interannual trends in the data. It was found that the model's strength lies in its consistency in predicting variables including temperature, sea level pressure, and wind direction and speed. Sea level pressure predictions prove especially accurate. In general the model's predictions are the weakest in the summer months.
A41A-06 0830h
Combining Multiple Atmospheric GCM Ensembles for Seasonal Prediction with a Bayesian Method
The skill of seasonal climate predictions can be enhanced by combining together the predictions made with different models, and it is desirable to be able to combine them in an optimal way. Here we develop an improved Bayesian optimal weighting scheme to combine six atmospheric general circulation model (GCM) seasonal hindcast ensembles. The approach is based on the prior belief that the forecast tercile-category probabilities are equal to the climatological ones. The six GCMs are integrated over the 1950-97 period with observed monthly SST prescribed at the lower boundary, and the scheme is applied to seasonal-mean simulations of precipitation as well as near-surface temperature. A key ingredient of the scheme is the climatological equal-odds forecast, which is included as one of the models in the multi-model combination. The weights of the individual models are determined by maximizing the log-likelihood of the combination by season over the integration period. Refinements are made to the original Bayesian scheme of Rajagopalan, Lall and Zebiak (2002), by reducing the dimensionality of the numerical optimization, averaging across data sub-samples, and including spatial smoothing of the likelihood function. These modifications are shown to yield increases in cross-validated Ranked Probability Skill Score (RPSS) skills. The Bayesian optimal weighting scheme is shown to outperform a simple unweighted pooling together of the models, which in turn outperforms the individual models. In the extratropics, the main benefit is to bring much of the large area of negative precipitation RPSS up to near-zero values. The skill of the optimal combination is almost always found to increase when the number of models in the combination is increased from 3 to 6, regardless of which models are included in the 3-model combination.
A41A-07 0830h
Large Improvement in CFS Prediction Skill by Regime Dependent Model Bias Corrections
The potential predictability of the recent NCEP coupled Climate Forecast System (CFS) is evaluated first, pin-pointing where on the globe the climate signals are likely to emerge and become potentially predictable. The real-time prediction problems are then looked into focusing on finding a new way to conduct model bias correction in order to raise the practical prediction skill. Large potential predictability can be found in the tropics, as expected. For the northern extra-tropical latitudes, only the Pacific/North American sector has some significant potential predictability. That predictability comes mainly from El Nino warm winters, much less from La Nina cold winters, and literarily none from ENSO neutral winters. The conventional model bias correction for prediction skill improvement deals with only a fixed difference: between model and observed climatology. We argue that the fact should be recognized that the bias of a climate model is sensitively dependent upon the prevailing circulation regime. At least, there are three major circulation regimes for our recent climate: the El Nino, La Nina, and ENSO neutral type of time-mean basic flows. We'll show that the model bias is distinct for a distinctive circulation regime. The regime dependent bias is also sensitive to prediction lead time. If we conduct regime dependent and lead time sensitive bias corrections, the practical prediction skill can be raised by a huge amount. A gain of at least 10 points in anomaly correlation metric for 1-month lead DJF predictions and a close to 20 points gain for 6 months lead predictions can be achieved.
A41A-08 0830h
Predictability of the Seasonal Climate Associated with ENSO in NCEP Climate Forecast System
The predictability of seasonal climate associated with ENSO is studied for NCEP Climate Forecast System (CFS) 23-year retrospective forecasts. Warm-minus-cold composites of the lead 1-6 month sea surface temperature (SST) anomalies show an ENSO-like horse-shoes pattern in the tropical Pacific, comparable with observation. There is a corresponding increased precipitation band along the equator near the dateline extending eastward to the South American coast, as well as the less precipitation over the Maritime Continents and off-equatorial western Pacific. Extended empirical orthogonal function (EEOF) analysis of the SST anomaly recovers ENSO -like dominant mode in the tropics for all seasons. Identification of patterns that optimize the signal-to-noise ratio is obtained by linear regression of the ensemble means on the principal component (PC) time series of SST. The optimized height patterns for boreal winter and spring are similar, although the winter response over the northern extratropics is somewhat weaker. Some subtle changes in amplitude are found in difference of leading initial conditions. The signal-to-noise ratio is significantly greater than unity in the Tropics (all seasons), the northern Pacific and continental North America subtropics (boreal winter and spring), and the southern Pacific subtropics (boreal fall).