Weather and climate are a constant in our lives, yet highly fickle. We expect accurate forecasts of the weather tomorrow or next winter but this is a challenge for scientists due to the large number of variables interacting in complex ways. A recent review article in Reviews of Geophysics discussed some of the frameworks being used to model and predict weather and climate. The editors asked one of the authors to explain some of the key terms in this field.
What are the different frameworks for understanding weather and climate?
There are three main ways of conceptualizing weather and climate: linear, modified linear and nonlinear. A linear approach assumes a direct and clear link between cause and effect. For example, the increase in winter mean Southern California rainfall (the effect) during most El Nino events is a direct response to the unusually warm ocean temperatures in the eastern tropical Pacific (the cause). This approach allows for an estimate of the size of the effect.
A modified linear approach would recognize the existence of noise, so that even if the cause (ocean temperature in our example) is perfectly known, there is still a range of uncertainty to the size of the effect. The term noise is applied to all phenomena that have an unpredictable influence on the effect, often because these phenomena occur on much shorter time scales. Thus in our example, the timing and path of individual storms are unpredictable beyond a week or two, and thus contribute to an uncertainty in winter mean California rainfall.
A nonlinear approach recognizes that the path between one hypothesized cause and a particular effect is in reality influenced by other phenomena (thus leading to multiple causes), and that the interplay between these causes can lead to very different effects. In our example, the California winter mean rainfall is known to depend on not only the ocean temperatures in the Eastern tropical Pacific, but also the ocean temperatures in the mid-latitude North Pacific, the two causes interacting in a possibly complex way.
What are the advantages of applying a nonlinear approach?
Nonlinear approaches allow for the full, possibly complex, interaction of many atmospheric and oceanic phenomena, thus coming to grips with nature in a more sophisticated, and more realistic manner. These phenomena include persistent high-pressure regimes (also called blocking), associated with such diverse phenomena as drought, extreme heat waves and extreme cold waves, the polar vortex (strength of the upper level low-pressure area lying near the Earth’s poles), and the storm tracks (the paths of mid-latitude storms). Their mutual interaction can lead to a great deal of year-to-year variability in the weather patterns, even in the absence of any strong “external” forcing signal (such as unusual tropical ocean temperatures). In the presence of external forcing, these interactions also modulate the path between cause and effect. Mathematical and statistical modeling of these interactions can lead to a better estimate of the uncertainty in predictions, whether they are predictions for the next month or next season.
How does the concept of “regimes” help to frame weather and climate research?
Regimes are patterns of winds and temperature over extended regions of the atmosphere (such as the entire Euro-Atlantic region) which are “preferred,” “persistent” or both. A persistent pattern is one which has a lifetime which is unusually long, while a preferred pattern is one which occurs more often than would be expected if the winds and temperature were governed by normal (that is Gaussian) statistics. They frame research in weather and climate in several ways.
The geographical distribution of rainfall, temperature and winds for a given period may depend strongly on which regime the atmosphere is in, as will the likelihood of extreme events (for example, cold snaps, heat waves, floods, or damaging winds) in any particular location. This would indicate that predicting the transitions between regimes is of great importance, although this remains very challenging even for today’s state-of-the-art numerical weather prediction models. How often the atmosphere resides in any given regime over a whole season may be strongly dependent on external forcing. Thus the probability of strong blocking over the Gulf of Alaska (a regime in the Pacific Ocean) is strongly decreased during El Nino winters. Thus we see that the actual expression of weather changes during these winters is determined by the changes in regimes.
What are the implications for weather and climate models?
The ability of atmosphere and coupled atmosphere-ocean models to capture the observed preferred and persistent states (regimes) in the winds and temperature is an important area of research. In terms of week-to-week weather forecasting, the question is how well the models can predict the shifts between regimes, starting from the known initial state. The answer to this depends on two factors: one is the realism of the regimes simulated by the model in long simulations without any information from observations, and the second is how intrinsically predictable the regimes actually are.
With regard to the first question, nearly all models in use do produce regimes; the question is how realistic they are. The answer to the second question is not completely known at the current time. For simulations of the future climate (with increased levels of carbon dioxide and other gases) we are entirely dependent on the models for guidance. Will the current regimes remain substantially the same, with possible modifications to their frequency of occurrence), or will the regimes themselves substantially change? Our confidence in models’ ability to answer these questions depends on their ability to simulate current regimes. This is an on-going question of importance.
—David M. Straus, Department for Atmospheric, Oceanic and Earth Sciences and Center for Ocean-Land Atmosphere Studies, George Mason University; email: dstraus@gmu.edu
Citation:
Straus, D. M. (2017), Concepts for dealing with the complexity of weather and climate, Eos, 98, https://doi.org/10.1029/2018EO074685. Published on 31 July 2017.
Text © 2017. The authors. CC BY-NC-ND 3.0
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