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GFDL Research Highlights

July 29th, 2019 - Seasonal prediction potential for springtime dustiness in the U.S.

Severe dust storms reduce visibility and cause breathing problems and lung diseases, affecting public health, transportation, and safety. Reliable forecasts for dust storms and overall dustiness are important for hazard preventions and resource planning. Most dust forecast models focus on short, sub-seasonal lead times, i.e., three to six days, and the skill of seasonal prediction is not clear. In this study we examine the potential of seasonal dust prediction in the U.S. using an observation-constrained regression model, with key variables predicted by a seasonal prediction model, GFDL’s Forecast-Oriented Low Ocean Resolution (FLOR). Read More…

July 18th, 2019 - Seasonal to multi-annual marine ecosystems prediction with a global Earth system model

Climate variations profoundly impact marine ecosystems and the communities that depend upon them. Anticipating these shifts using global Earth System Models (ESMs) could enable communities to adapt to climate fluctuations and contribute to long-term ecosystem resilience. The authors show that newly developed ESM-based marine biogeochemical predictions can skillfully predict observed seasonal to multi-annual chlorophyll fluctuations in many regions. The authors also provide an initial assessment of the potential utility of such predictions for marine resource management. Read More…

July 18th, 2019 - A Review of the Role of the Atlantic Meridional Overturning Circulation in Atlantic Multidecadal Variability and Associated Climate Impacts

This paper provides a comprehensive review of the linkage between multidecadal Atlantic Meridional Overturning Circulation (AMOC) variability and Atlantic Multidecadal Variability (AMV) and associated climate impacts, by synthesizing recent studies that employed a wide range of approaches (modern observations, paleo reconstructions, and climate model simulations). The AMOC, which includes a northward flow of warm salty water in the upper Atlantic and a southward flow of the transformed cold fresh North Atlantic Deep Water in the deep Atlantic, transports a huge amount of heat northwards in the Atlantic. There is strong observational and modeling evidence that multidecadal AMOC variability is a crucial driver of the observed AMV and associated climate impacts, and an important source of enhanced decadal predictability and prediction skill. Read More…

May 17th, 2019 - Dynamical Seasonal Prediction of Tropical Cyclone Activity: Robust Assessment of Prediction Skill and Predictability

Dynamical seasonal prediction systems have recently shown great promises in predicting tropical cyclone activity. GFDL’s Forecast–oriented Low Ocean Resolution (FLOR) model (Vecchi et al. 2014) provides experimental predictions to National Centers for Environmental Prediction (NCEP) each month as part of the North American Multi-Model Ensemble (NMME) project. The current study analyzes this state-of-the-art prediction system and offers a robust assessment of when and where the seasonal prediction of tropical cyclone activity is skillful. Read More…

April 26th, 2019 - Prominence of the tropics in the recent rise of global nitrogen pollution

In this study, GFDL’s Land Model (LM3-TAN) was used to analyze the past two and half centuries of land nitrogen storage, fluxes, and pollution to the ocean and atmosphere, considering not only the effect of increased anthropogenic reactive nitrogen (e.g., synthetic fertilizers and atmospheric deposition associated with agricultural industrialization and fossil fuel combustion) inputs, but also the effects of elevated atmospheric CO2, land use and land cover change, and climate change. The results show that globally, land has served as a net nitrogen sink since the late 1940s, buffering coastal waters against eutrophication and society against greenhouse gas-induced warming. Read More…

March 29th, 2019 - Toward Convective-Scale Prediction within the Next Generation Global Prediction System

Prediction of convective-scale storms, such as severe thunderstorms or tornadoes, has been traditionally performed with limited-area models. Issues related to the limited extent of the domain and the external boundary conditions remain significant challenges, so a global convection-permitting model without side boundaries is potentially more advantageous for mesoscale prediction. However, present-day computing resources are insufficient to support real-time global convective-scale weather prediction. Read More…

March 26th, 2019 - An assessment of the predictability of column minimum dissolved oxygen concentrations in Chesapeake Bay using a machine learning model

In parts of many estuaries and other coastal areas, such as the Chesapeake Bay, the concentration of oxygen dissolved in the water regularly drops to a value so low that many species of fish, crabs, and other ecologically and economically important creatures are unable to live. This condition, known as hypoxia, is often driven by warm temperatures and other climate conditions. Subseasonal to seasonal scale forecasting models, including those developed by GFDL, have shown skill at forecasting variations in temperature and other drivers of hypoxia up to several months in advance. Translating these forecasts into skillful forecasts of hypoxia could enable improved management of fisheries, reduce fishing effort, and allow more adaptive management of water quality. Read More…

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