“This research addresses the critical need to improve our understanding of how land surface initialization and land-atmosphere interactions influence subseasonal to seasonal (S2S) predictability of extreme heat and heat waves over North America. Accurate forecasting of extreme heat events, particularly on S2S timescales, is important for public health preparation as vulnerability to extreme heat has increased over time. Soil moisture anomalies, through their control on the partitioning of sensible and latent heat fluxes, are linked to temperature extremes. Dry soils limit evapotranspiration and can establish and perpetuate extreme heat events through atmospheric heat accumulation (e.g., Miralles et al. 2014). Therefore it is not surprising antecedent soil moisture deficits are found to correspond strongly with extreme temperatures in most regions of the world. Recent studies have demonstrated large spread in model forecasts and simulation of heat wave events over Europe and North America. Significant inter-model variability is purported to be a potential consequence of different boundary layer and convective parameterizations, land surface treatments, and coupled land-atmosphere model sensitivity. To date, few studies have explicitly evaluated the influence of the land surface and its initialization on model predictions of heat waves at S2S time scales.
The influence of antecedent drought conditions is particularly important in North America as past heat wave events may be established and prolonged by both advection of warm, dry air and limitation of local moisture recycling due to dry soils. The strong connection between the land surface and subsequent extreme heat offers promise that realistic soil moisture initialization could improve model forecast skill. Indeed, previous results over the contiguous United States suggest the land surface has a significant impact on extreme heat forecasts, particularly during boreal summer (Ford and Quiring, 2014). However, there is still a lack of consensus about: (1) the role of antecedent drought conditions in forcing heat waves over North America (2) the ability of numerical forecast models to predict extreme heat events at S2S time scales, and (3) the importance of realistic land surface initialization and model fidelity for accurate and timely extreme heat predictions. The goal of this project is to enhance our understanding of the connection between droughts and heat waves in the United States, as well as evaluating the ability of a suite of climate forecast models to predict heat wave occurrence. This goal will be achieved by addressing three main objectives:
(1) Evaluate the ability of numerical forecast models included in the Sub-seasonal to Seasonal (S2S) Prediction and North American Multi-Model Ensemble (NMME) Phase II projects to predict heat waves following drought events in the United States
(2) Relate model forecast performance to parameterization of land surface variables, coupled land-atmosphere metrics and initialization of land surface conditions
(3) Assess how more realistic land surface initialization in forecast models influences their ability to predict and simulate heat wave events in the United States
This project specifically addresses the MAPP Competition 2 priority area of addressing the predictability of S2S phenomena in the context of extremes and their key underlying physical processes. We will be using reforecast datasets from the S2S Prediction Project and the North American Multi-Model Ensemble project. Our project goals are closely aligned with the mission of the Climate Prediction Task Force of achieving significant new advances in current capabilities to understand and predict intra-seasonal to inter-annual climate variability. In addition, the objectives of this research addresses the NOAA high priority of the S2S prediction gap (NWS Goal 3, element 1.20 of the Strategic Plan) as well as NOAA’s goals for leadership in science and innovation. Finally the contributions of this project to the MAPP S2S Task Force address the important issues of S2S predictability and prediction of extreme heat in the context of land-atmosphere coupling and model land surface initialization.”
Climate Risk Area: Extreme Heat