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GFDL Research Highlights

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…

December 3rd, 2018 - Natural variability of Southern Ocean convection as a driver of observed climate trends

Observations show that Arctic sea ice is rapidly declining, but observations also clearly show an expansion of Southern Ocean (SO) sea ice extent during the satellite era (1979 to the present). This modest increase is consistent with an observed SO cooling trend. The sea surface temperature (SST) and sea ice concentration (SIC) trends are not homogeneous in space, with opposing signs in the Amundsen-Bellingshausen Seas versus the Ross and Weddell Seas. Read More…

November 19th, 2018 - Change in future climate due to Antarctic ice melt

Ice sheet melt is a known neglected forcing in climate model simulations, contributing to uncertainties in climate projections. This is the first study to directly implement estimates of Antarctic ice sheet melt in a climate simulation, showing the actual change in climate projections due to the freshwater input. The authors used a large ensemble to confidently separate the freshwater signal from natural variability and show when we can expect these freshwater-induced effects to become significant. Read More…

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