A central goal of the sea ice research community is to assess the ability of climate models to accurately predict Arctic sea ice. A broad range of stakeholders have a pressing need for regional forecasts. Previous studies assessing sea ice prediction skill suggest that some regions in the Arctic have a “prediction skill barrier” in the spring season, where predictions of summer sea ice made prior to May are substantially less accurate than predictions made after May. However, this barrier has only been documented in a few climate models. This study employs a simple model that uses sea ice volume to predict summer sea ice area. Read More…
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…
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…
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…
A recent study found a downward trend from 1949-2016 in the speed at which tropical cyclones move. If this could be attributed to climate change the implications would be enormous. Slower moving storms, as exemplified by Hurricane Harvey in 2017, have the potential to produce much more rainfall than faster ones. Read More…
When using European Centre for Medium‐Range Weather Forecasts (ECMWF) initial conditions, a new global weather model built at NOAA’s Geophysical Fluid Dynamics Laboratory produces better hurricane forecast skill than the world‐leading European model. Read More…
The Atlantic Multidecadal Variability (AMV) has profound climate impacts. Improvement of our understanding of the AMV mechanisms is crucial for successful future prediction of AMV and associated climate impacts, with enormous social and economic implications. Read More…
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…
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…
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…
