New insights into predicting future droughts in California: Natural cycles, sea surface temperatures found to be main drivers in ongoing event

According to a new NOAA-sponsored study, natural oceanic and atmospheric patterns are the primary drivers behind California's ongoing drought. A high pressure ridge off the West Coast (typical of historic droughts) prevailed for three winters, blocking important wet season storms, with ocean surface temperature patterns making such a ridge much more likely. Typically, the winter season in California provides the state with a majority of its annual snow and rainfall that replenish water supplies for communities and ecosystems.

Further studies on these oceanic conditions and their effect on California's climate may lead to advances in drought early warning that can help water managers and major industries better prepare for lengthy dry spells in the future.

"It's important to note that California's drought, while extreme, is not an uncommon occurrence for the state. In fact, multi-year droughts appear regularly in the state's climate record, and it's a safe bet that a similar event will happen again. Thus, preparedness is key," said Richard Seager, report lead author and professor with Columbia University's Lamont Doherty Earth Observatory.

This report builds on earlier studies, published in September in the Bulletin of the American Meteorological Society, which found no conclusive evidence linking human-caused climate change and the California drought. The current study notes that the atmospheric ridge over the North Pacific, which has resulted in decreased rain and snowfall since 2011, is almost opposite to what models project to result from human-induced climate change. The report illustrates that mid-winter precipitation is actually projected to increase due to human-induced climate change over most of the state, though warming temperatures may sap much of those benefits for water resources overall, while only spring precipitation is projected to decrease.

The report makes clear that to provide improved drought forecasts for California, scientists will need to fully understand the links between sea surface temperature variations and winter precipitation over the state, discover how these ocean variations are generated, and better characterize their predictability.

This report contributes to a growing field of science-climate attribution-where teams of scientists aim to identify the sources of observed climate and weather patterns.

"There is immense value in examining the causes of this drought from multiple scientific viewpoints," said Marty Hoerling, report co-author and researcher with NOAA's Earth System Research Laboratory. "It's paramount that we use our collective ability to provide communities and businesses with the environmental intelligence they need to make decisions concerning water resources, which are becoming increasingly strained."

To view the report, visit:?http://cpo.noaa.gov/MAPP/californiadroughtreport.

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Predicting climate: Researchers test seasonal-to-decadal prediction

In a new study published in Tellus A, Francois Counillon and co-authors at the Bjerknes Centre are testing seasonal-to-decadal prediction.

At the Bjerknes Centre, researchers are exploring the potential for seasonal to decadal climate prediction. This is a field still in its infancy, and a first attempt was made public for the latest Intergovernmental Panel on Climate Change (IPCC) report.

Apart from a few isolated regions, prediction skill was moderate, leaving room for improvement. In a new study published in Tellus A, seasonal-to-decadal prediction is tested with an advanced initialisation method that has proven successful in weather forecasting and operational oceanography.

"Ordinary" climate projections are designed to represent the persistent change induced by external forcings. Such "projections" start from initial conditions that are distant from today's climate and thus fail to "predict" the year-to-year variability and most of the decadal variability -- such as the pause in the global temperature increase (hiatus) or the spate of harsh winter in the northern hemisphere. In contrast, weather predictions rely entirely on the accuracy of their initial state as the influence of the external forcing is almost imperceptible.

For seasonal-to-decadal time scales both the initial state and the external forcing influence the prediction. Starting a climate prediction from an initial state closer to the real climate is therefore necessary to yield better prediction than accounting only for external forcing. In our region of interest, decadal skill may be achieved by improving the representation of the heat content transiting into the Nordic Sea and in turn may influence the precipitation and temperature over Scandinavia.

The method employed to initialise/ correct a dynamical system is referred to as data assimilation. It estimates the initial state of a model knowing a set of sparse observations (much less than 1% of the ocean variables are observed). A relationship between the observations and the non-observed variables must be found to broaden the corrections.

Furthermore, the corrections must satisfy the model dynamics to avoid abrupt adjustments during the forecast. The Ensemble Kalman Filter uses statistics from an ensemble of predictions to estimate the relationship between the observations and all variables for their correction. This method is computationally intensive as it requires parallel integrations of the model but it ensures that the relationship evolve with the system, and that the corrections satisfy the dynamics of the model.

The Norwegian climate prediction model (NorCPM) combines the Norwegian Earth System model with the Ensemble Kalman Filter. In time, we intend to perform retrospective decadal predictions (hindcasts) over the last century, to test the skill of our system on disparate phases of the climate and shed light on the relative importance of internal and external influences on natural climate variability, including the significance of feedback mechanisms. Sea surface temperatures (SST) are the only observations available for such a long period of time and will be used for initialisation.

Our study investigates the potential skills of assimilating SST only, using an idealised framework, i.e. where the synthetic solution is taken from the same model at different times. This framework allows an extensive validation because the full solution is known and our system can be evaluated against the upper predictive skill (the case where observations would be available absolutely everywhere). NorCPM demonstrated decadal predictability for the Atlantic meridional overturning and heat content in the Nordic Seas that are close to the model's limit of predictability. Although these results are encouraging, the idealised framework assumes that the model is perfect and lower skill is expected in a real framework. This verification is currently ongoing.

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Scientists a step closer to predicting tornadoes

For decades, meteorologists have been able to forecast the severity of hurricane seasons several months ahead of time. Yet forecasting the likelihood of a bad tornado season has proved a far greater challenge.

By Charlie Riedel, AP

Brenna Burzinski looks through the rubble in her devastated apartment in Joplin, Mo., on May 25.

By Charlie Riedel, AP

Brenna Burzinski looks through the rubble in her devastated apartment in Joplin, Mo., on May 25.

Now, research from scientists at Columbia University's International Research Institute for Climate and Society could eventually lead to the first seasonal tornado outlooks.

"Understanding how climate shapes tornado activity makes forecasts and projections possible, and allows us to look into the past and understand what happened," said Michael Tippett, lead author of a study in February's journal of Geophysical Research Letters.

The need for such data is reinforced by the still-fresh memory of 550 Americans killed by tornadoes last year — coupled with an unusually violent January for twisters.

In the study, Tippett and his team looked at 30 years of past climate data. They used computer models to determine that the two weather factors most tied to active tornado months and seasons were heavy rain from thunderstorms and extreme wind shear (wind blowing from different directions at different layers of the atmosphere).

"If, in March, we can predict average thunderstorm rainfall and wind shear for April, then we can infer April tornado activity," Tippett says.

The method worked for each month except for September and October, and it worked best in June.

This is the first time a forecast of up to a month in advance has been demonstrated, he says.

"A connection between La Niña and spring tornado activity is often mentioned," Tippett says, "but such a connection really has not been demonstrated in the historical data and hasn't been shown to provide a basis for a skillful tornado activity forecast.

"Our work bridges the gap between what the current technology is capable of forecasting (large-scale monthly averages of rainfall and winds) and tornado activity, which the current technology cannot capture," he says.

The research isn't ready for prime time yet, however, so no official forecast will be made for the upcoming season using these methods.

"This is a useful first step," says Harold Brooks of the National Oceanic and Atmospheric Administration, who was not involved in the study. He says it will be helpful to know, for example, that sometime in the last week of April, conditions will be favorable for lots of tornadoes in the eastern USA.

With greater lead time, a state emergency planner "could be better prepared with generators and supplies," Brooks says.

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