Current dynamic seasonal predictions show lower prediction skills over the US Great Plains than over the eastern and western United States (US). This is particularly so over that region during the warm season (May through August). The dynamical seasonal prediction systems have virtually no prediction skill, for the warm season rainfall over this region. This lack is in large part because the predictability provided by ENSO and tropical Atlantic sea surface temperature anomalies (SSTA) becomes weaker in the warm season. Internal atmospheric variability has significant impact on warm season rainfall, but without strong feedbacks, it can only sustain an anomalous drought circulation for a few weeks. Many studies have suggested land surface feedbacks as the main source of the dry memory. However, such feedbacks cannot sustain dry memory for much more than a month.
Extreme droughts are resulted from dry memory lasting from 3 to 8 months. An empirical model based on such drought memory shows higher skills for seasonal prediction of warm season rainfall anomalies than those of the dynamic models over the US Great Plains. Thus, there is more predictability than that shown by the current dynamic prediction system. However, the processes behind such higher empirical predictability are unclear, limiting our ability to tap into it as an additional source of predictability for the warm season drought. This proposal addresses this knowledge gap by clarifying the following questions:
- How does the interplay between anomalous land surface conditions, shallow and deep convection, diabatic heating, and large-scale anticyclonic circulation influence the onset, duration and demise of the drought on a seasonal scale over the Great Plains?
- What role do sub-seasonal atmospheric variability play in the onset and demise of the drought memory on a seasonal scale?
- What are the causes of drought memory in dynamic models such as those of CFSv2, CCSM4, CESM1 being weak and short-lived?
We will use CFSR, MERRA, NARR reanalyses, and NLDAS land surface product to diagnose the large-scale atmospheric and land surface anomalous conditions. We will also use long-term ground based and satellite datasets, such as the NCDC rainfall data, the North American Soil Moisture Database (NASMD) data, the Vegetation Drought Response Index (VegDRI), the Sun Induced Fluorescence (SIF), CloudSat data, to diagnose the anomalous conditions of cloud, rainfall, surface temperature, soil moisture and vegetation. The Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) atmospheric measurements and Oklahoma Mesonet will be used for detailed process studies. We will evaluate the seasonal predictions from the CFSv2, CCSM4 and CESM1 of the North-American Multi-Model Ensemble (NMME) prediction system.
Climate Risk Area: Water Resources