Attribution and Predictability Assessments
Lead: Tom Hamill
Policy and decision makers seek accurate knowledge of regional and seasonal differences in climate trends and variations for determining impacts and adaptation decisions in agriculture, water supply, health, energy and other sectors. They also desire the best available science regarding the factors causing high-impact weather and climate related extremes to make informed decisions on how society should invest in critical infrastructure in risk-prone areas while ensuring resilience.
PSL's Attribution and Predictability Assessments Team seeks to understand the predictability of and to improve predictions of extreme phenomena, especially on sub-seasonal to seasonal time scales. Additionally, APA conducts attribution studies of extreme weather phenomena describes the predictability characteristics of those extreme events and identifies sources of their predictability. We place a special emphasis on understanding the large-scale drivers that influence local and regional extreme events such as floods, droughts, and heat waves. APA also focuses on the statistical postprocessing of forecasts, whereby discrepancies between past forecasts and observations/analyses are used to make corrections to the real-time forecasts. APA actively partners with the National Weather Service, providing experimental algorithms to operational partners at the Climate Prediction Center, the Meteorological Development Laboratory, the Environmental Modeling Center, and more.