The NOAA CPO Modeling, Analysis, Prediction, and Projections (MAPP) program will host a webinar on the topic of Drought Research: Improved Understanding, Monitoring, and Prediction of Drought on Friday, May 30. The announcement is provided below; you are invited to remotely join the session.
Rong Fu -- Explore mechanisms behind the spring to summer drought memory and potential for early warning of summer drought over US Southern Plains -- Dynamic predictions, such as those produced by climate forecast system (CFS), and the national multi-models ensemble experiment (NMME), could not predict summer droughts in 2011 and 2012 and generally do not have more prediction skill than the persistence of the seasonal rainfall cycle or the baseline. In contrast, observations show a significant correlation between the strong dry anomalies in spring and summer over the US southern Great Plains. What processes are responsible for such apparent memory and is it possible to provide an early warning of the summer drought based anomalous large-scale atmosphere and land surface conditions in spring? We will present preliminary results to explore these questions. These results suggest that:
a) Only 1/5 of the La Niñas have led to summer droughts over the Southern Great Plains. These La Niña events showed a distinctively different large-scale circulation pattern in spring from those without associated with summer droughts.
b) Strong dry anomalies in spring reduce convection, cloudiness and atmospheric humidity, which in turn increase longwave cooling and mid-tropospheric subsidence. In absent of strong remote forcing, these atmospheric feedbacks could maintain and re-enforce the anomalous mid-tropospheric high. In doing so, they provide dry memory from spring to summer over the southern Great Plains.
c) A multivariate EOF based empirical model appears to show more skills than the baselines in predicting summer drought based on the large-scale atmospheric circulation anomalies and land surface/soil moisture conditions in spring.
Chunzai Wang -- Impact of the Atlantic Warm Pool on North American Rainfall – This talk uses observations and coupled model experiments to show that North American rainfall response to the Atlantic Warm Pool (AWP) is very different during the warm and cold seasons. In the warm season, a large AWP leads to a “Gill-type” response that weakens the North Atlantic subtropical high. The resulting influence over North America is anomalous southward winds and cold advection in the low troposphere, which lead to an anomalous subsidence, a reduced moisture flux convergence, and hence a suppression of rainfall in the central United States. In the cold season, however, the influence of the AWP on North American rainfall is through the teleconnection of AWP-induced SST cooling in the tropical Pacific. A large AWP strengthens the regional Hadley circulation from the AWP to the tropical southeastern Pacific (SEP) in the boreal summer. This meridional circulation and associated subsidence enhance the South Pacific subtropical high, which in turn leads to an increase of the low cloud, a strengthening of the easterly trade wind and thus a cooling of the SEP SST. Due to the wind-evaporation-SST feedback, the SEP SST cooling gradually propagates equatorward to the tropical central and western Pacific, which results in an SST pattern similar to that of La Niña events during the cold season. The La Niña-like SST response in the tropical Pacific further triggers a negative phase of the Pacific North American teleconnection pattern, with a high pressure in the North Pacific, a low pressure over Canada and a high pressure in the south United States. The high pressure in the south U.S. favors a surface divergence, an anomalous subsidence and thus a dry condition over the southern part of United States. The opposite is true for a small AWP.
Christa Peters-Lidard -- The impact of assimilating terrestrial remote sensing observations on drought monitoring -- The accurate knowledge of soil moisture and snow conditions is important for the skillful characterization of agricultural and hydrologic droughts, which are deemed as deficits of soil moisture and streamflow, respectively. This article examines the influence of remotely sensed soil moisture and snow depth retrievals towards improving estimates of drought through data assimilation. Soil moisture and snow depth retrievals from a variety of sensors (primarily passive microwave-based) are assimilated separately into the Noah land surface model during a period of 1979 to 2011, over the continental United States, in the North American Land Data Assimilation System (NLDAS) configuration. The model runs without data assimilation (open loop) were found to have high skills compared to observations due to the high quality NLDAS forcing data. Overall, the assimilation of soil moisture and snow data sets was found to provide marginal improvements over this open loop configuration. Though the improvements in soil moisture fields through soil moisture data assimilation were barely at the statistically significant levels, these small improvements were found to translate into subsequent small improvements in simulated streamflow. Similarly, assimilation of snow depth datasets were found to generally improve the snow as well as streamflow, with some notable degradations observed in the Western U.S. A quantitative examination of the percentage drought area from root zone soil moisture and streamflow percentiles was conducted against the U.S. Drought Monitor data. Our results suggest that soil moisture assimilation is effective in providing improvements at short time scales, both in the magnitude and representation of the spatial patterns of drought estimates, whereas the impact of snow data assimilation was marginal and often disadvantageous.