MAPP Webinar

Abstracts:

Eric Wood — Recent results and challenges related to drought prediction — The impacts from drought are perhaps the highest among all natural disasters. In the United States there are a number of efforts to improve the monitoring and forecasting of drought, in part to improve the ability to provide information for improved coping and management of drought.  Globally, the Group of Earth Observations (GEO) and the World Climate Research Programme have activities related to improved monitoring and forecasting.  This seminar will present recent research results that point to a number of challenges related to drought prediction.  These include: (1) how can we best determine the probability of drought recovery?  (2) What do we know about the sources of hydrologic predictability, and are there limits?  And (3) How important is land-atmospheric coupling for drought predictability?

Bradfield Lyon — Seasonal Prediction of Meteorological Drought Using Statistical-Dynamical Approaches — An essential feature of drought is prolonged, deficient precipitation relative to the expected climate. This feature is the basis for precipitation based meteorological drought indices, which integrate precipitation variability over differing time periods (e.g., 3, 6, 12months, etc.) in an attempt to capture multiple aspects of land surface hydrology such as soil moisture at different depths, streamflow, and so on that are affected by precipitation variability on different time scales. The cumulative, time-integrated nature of meteorological drought events often results in considerable persistence from one month to the next, a characteristic that is valuable for both drought monitoring and prediction.  In addition, when utilizing seasonal precipitation forecasts from a dynamical model (or ensemble of models, such as the NMME) to make predictions of meteorological drought indicators it is often necessary to “merge” the precipitation forecast with observed conditions.  This necessitates removing the baseline skill to evaluate any value added by the model.  In practice, the initial condition often also serves as a substantial source of predictive skill of the index in its own right.

In this talk we first summarize the baseline skill that is obtained from the inherent persistence characteristics of meteorological drought indicators.  Emphasis is placed on the standardized precipitation index (SPI), but any meteorological drought indicator can be similarly assessed.  For the case of the SPI we show how seasonality in climatological precipitation modulates this baseline skill, and also show how the latter can be further optimized.  We then show where the CFSv2 dynamical model precipitation forecasts offer additional skill relative to the baseline when predicting the SPI.  We consider first the influence of just sea surface temperatures (using only the atmospheric component of the model, the GFS) and then when the land surface and atmospheric conditions are also initialized in the coupled version of the model.  Finally, we show some examples of potentially increasing dynamical model forecast skill (for precipitation and temperature) through the use of model output statistics (MOS).

Siegfried Schubert — The Role and Predictability of Stationary Rossby Waves — This study examines the nature and predictability of summertime (JJA) stationary Rossby Waves with a focus on the impacts on precipitation (P) and surface temperature (Tsfc) over North America.  MERRA is used to quantify the structure of the waves, their links to P and Tsfc and, together with a stationary wave model, identify their primary forcing.   The predictability of three extreme events are analyzed (June 1988, June 1998, and June 2012) for which stationary Rossby Waves appear to play an important role in determining the surface temperature and precipitation extremes.  For each case, an ensemble of 32 hindcasts were performed with the GEOS-5 AGCM (initialized on May 20 with small perturbations introduced in the atmosphere) and forced with the observed SST.   The results indicate significant predictability associated with these waves extending for several weeks to one month, though model deficiencies in the simulation of the mean state (in particular the mean summer jets) appear to currently limit our ability to fully harvest that predictability.

Kingtse Mo — Objective Drought Classification — Drought is a complex phenomenon. To monitor drought in real time, we use physically based indices. Standardized Precipitation Indices (SPIs) are used to monitor the precipitation deficits. Soil Moisture (SM) Percentiles (SMP) and Standardized runoff indices (SRI) are employed to monitor soil moisture and runoff deficits respectively. Because long term observations of runoff and soil moisture are not available, SM and runoff are often derived from the North American Data Assimilation system (NLDAS). There are large uncertainties in the NLDAS systems and different indices have different time scales. While all indices are likely to detect the same drought event and capture its evolution, differences are often too large to classify drought into D0 to D4 categories.

We propose to use a probabilistic approach to address the uncertainties of drought classification. We use the grand ensemble mean of 6-month SPI (SPI6), SMP and 3-month SRI(SRI3) from different NLDAS systems and models as an index for drought classification. The uncertainties of the grand mean index are assessed by using the drought occurrence probability defined as the percentage of indices in each drought category (D0 to D4).  The ensemble mean and the drought occurrence probability are used together for monitoring of drought development.  The new framework takes into consideration of uncertainties in indices and provides the regional information on drought development.