PREDICTABILITY & PREDICTION
OF WEATHER & CLIMATE
Can we predict the weather two weeks from today? Or two years from today? Or two decades from today?
These are the kinds of questions we study at George Mason's Department of Atmospheric, Oceanic, and Earth Sciences. Of course, it is not possible to predict the specific weather beyond two weeks, but we can predict the probability of certain weather events over extended periods of time. Recent and ongoing projects study the predictability of global phenomena on a range of time scales. We use global climate and weather models, machine learning, and advanced statistical techniques to perform this research.
SUBSEASONAL TO SEASONAL PREDICTIONS
In 2017, we published a paper that showed that the average temperature over certain parts of the United States could be predicted out to weeks 3-4 (DelSole et. al. 2017) https://www.insidescience.org/news/breaking-new-ground-weather-forecasting. Ordinarily, the probability of above or below normal temperature is 50
i-50, just like predicting heads on a coin flip. Using a forecast model, above or below normal temperatures could be predicted correctly 67 percent of the time-- much better than a coin flip.
MIDLATITUDE VARIABILITY ON INTRASEASONAL TO SEASONAL TIMESCALES
Variability of the climate system as a whole is characterized by a continuous distribution across all frequencies/timescales (Hasselmann 1976) with salient features at regional scales. The midlatitudes are characterized by a pronounced seasonal cycle of weather types driven by the annual insolation forcing, internal dynamics of the atmosphere, and tropical teleconnections. Recent results by Stan and Krishnamurthy (2019) show that similarly to the tropics, the midlatitudes are characterized by variability on intraseasonal time scales (10 to 100 days). This variability manifests as nonlinear oscillation that propagates in the zonal direction around the globe and meridionally in certain regions. We are focused on 1) developing and applying data adaptive methods to isolate these oscillations in observations and model simulations; 2) design and conduct numerical experiments for explaining the mechanisms that drive the variability of midlatitudes on these timescales; 3) apply their source of predictability to improve the forecast skill of weather prediction on subseasonal to seasonal time scales.
The slowly evolving states of the land surface are a source of predictability on a range of time scales. Anomalies in soil moisture, snow cover, vegetation phenology (greenness, leaf density, plant height) and soil temperature can affect the atmosphere above. In a forecast, the effects are felt as soon as the sun rises, as land surface states affect how much solar radiation is absorbed, and how it is partitioned between warming the ground, evaporating water (including transpiration by plants conducting photosynthesis) and directly warming the air near the surface. Numerical studies by Prof. Paul Dirmeyer [link: http://cola.gmu.edu/dirmeyer/] and colleagues have shown the peak impact on forecast skill is at a lead time of 7-14 days, meaning the quality of land surface initialization and coupled land-atmosphere models directly impacts both weather and subseasonal forecasts. However, the ability of the land to affect the atmosphere varies in space and time, and depends both on the sensitivity of surface fluxes to land states and the responsiveness of the atmosphere to variations in surface fluxes. The animation [can imbed image: http://cola.gmu.edu/dirmeyer/animation.gif] depicts how this varies around the world across the seasonal cycle: in blue areas the atmosphere is responsive but fluxes are insensitive to soil moisture variations; green areas show the opposite combination; only in red areas is there normally a coupled feedback loop whereby atmospheric temperature, humidity, clouds and precipitation may respond to soil moisture variations. However, in extreme situations like droughts, regions that are normally uncoupled like Northern Europe or Eastern China may experience land-atmosphere feedbacks, which may exacerbate drought conditions.
EXTREME AND HAZARDOUS WEATHER
Predicting California Drought
A recently completed modeling study used both NOAA’s seasonal prediction model and the CESM model of NCAR to assess whether the very unusual California drought associated with the 2015 – 2016 El-Nino was predictable on the basis of knowledge of the ocean temperatures beforehand. Using simulations with added tropical heating (to ensure the correct forcing), we concluded that the drought was the result of intra-seasonal variability which is very difficult to predict at long range. (https://doi.org/10.1175/JAS-D-19-0064.1) ole of the Indian Ocean heating, and at designing techniques to improve the Monsoon break and active spell cycle.
Predicting Extreme Weather using Circulation Regimes
A new approach linking large-scale circulation regimes to the probability of extreme weather events is being investigated with specific application to the forecast of extremes by the National Weather Service at an extended range (3 to 4 weeks). We seek to determine if the forecast models in use capture the observed preferred circulation regimes in the Pacific – North American region, and whether the association of these regimes with particular patterns of extreme weather (https://link.springer.com/article/10.1007/s00382-018-4409-7) can be exploited in the prediction of extreme weather. In related work, we are also examining the predictability of transitions between circulation regimes in the Euro-Atlantic region based on tropical forcing, in particular over the Atlantic Ocean.
We also study how well we can predict two decades or more into the future. A key problem in this area is quantifying how much the earth will warm due to increases in greenhouse gas concentrations. This problem is not straightforward because other mechanisms, particularly man-made aerosols and solar and volcanic activity, also influence the climate and therefore confound our ability to use historical data to infer the future. We are exploring novel approaches to isolating the warming due to human activities, including using data sets that are not normally used for this purpose.
Using similar techniques, we are studying the mechanisms by which tropical heating in the Indian and Pacific ocean basins influence the summer Monsoon over India. Early results show that substantially reducing the mean bias in the tropical heating in the CFSv2 model improves the equatorial wind patterns, but does not improve the forecast of summer mean rainfall. Further analysis and simulations are aimed at understanding the role of the Indian Ocean heating, and at designing techniques to improve the Monsoon