The 2018 tropical cyclone (TC) season in the North Pacific was very active, with 39 tropical storms including 8 typhoons/hurricanes. Unlike the typical limitations in skill of seasonal predictions made before April initial forecasts, the active 2018 TC season was successfully predicted by the Geophysical Fluid Dynamic Laboratory Forecast-oriented Low Ocean Resolution (FLOR) global coupled model 3–5 months in advance (i.e., successful predictions from 1 February 2018). Read More…
GFDL Research Highlights
In many parts of the world, water resources for humans and ecosystems are heavily dependent on precipitation. Terrestrial precipitation is fed by moisture originating as evaporation from oceans and from recycling of water evaporated from continental sources. Understanding the vulnerability of regional precipitation to changing climatic conditions and to changing land cover conditions is of critical importance to society. Read More…
In 1970, a new hurricane project was established at GFDL to perform basic hurricane research using numerical modeling. Within a few years, this pioneering research had led to the development of a new hurricane model. As the reputation of the model grew, GFDL was approached in 1986 by the director of the National Meteorological Center to establish a collaboration between the two Federal organizations to transition the model into an operational modeling system. After a multi-year effort by GFDL scientists to develop a system that could support rigorous requirements of operations, and multi-year testing had demonstrated its superior performance compared to existing guidance products, the model became operational in 1995. Through additional collaborations between GFDL and the U.S. Navy, the model was also made operational at Fleet Numerical Meteorology and Oceanography Center in 1996. Read More…
Existing hurricane prediction systems fall into two categories: hurricane track and intensity predictions on a weekly timescale; and the prediction of hurricane activity on a seasonal timescale. Substantial progress has been made in improving the predictions on the two distinct timescales in the past decade. However, the prediction of hurricane activity on a subseasonal timescale (from two weeks to two months) has not shown much advancement. Credible subseasonal hurricane predictions can have significant socioeconomic impacts, but are challenging. There is much uncertainty in the sources of predictability; furthermore, the realistic simulation of hurricanes requires high horizontal resolution (at least finer than 10 km), which is expensive when using global prediction systems. Read More…
The factors contributing to heatwaves have been the subject of intensive research for many decades. The urgency of this work arises from the steep toll that heatwaves impose on public health, and the prospect that climate change may increase the frequency and severity of these events. Heatwaves also occur beneath the waves, where they can severely affect living marine resources upon which our coastal economies and food supply relies. Read More…
This research explored the sensitivity of large-scale surface climate and tropical cyclone activity to a doubling of CO2, using three coupled global climate models that span a range of horizontal atmospheric and land resolutions. The authors investigated the impact of resolution changes in the atmosphere within a family of coupled global climate models with identical ocean and sea ice components, and whose atmospheric configurations differ only in their horizontal resolution (~200km, ~50km, and ~25km). Read More…
August 1st, 2019 - A spring barrier for regional predictions of summer Arctic sea ice
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…
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…
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…