Estimation of Atmospheric Predictability by Circulation Analogs

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  • 1 Cooperalive Institute for Research in Environmental Sciences, University of Colorado, Boulder, Colorado
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Abstract

An empirical predictability study is presented based on 7OO-hPa Northern Hemispheric circulation analogs. A linear relationship between the initial root-mean-square difference of analog pairs and the time taken for the error to reach a certain limit value is used to extrapolate the predictability with initial errors considerably smaller than those in the present database. The relationship, first used in predictability experiments with the NMC numerical weather prediction (NWP) model, conforms to the experimental data in that the effor growth depends not only on the magnitude of the error but also, to a lesser extent, on the initial error.

Despite the fact that earlier error growth studies did not reflect this dependence on the initial error predictability results with two state-of-the-art numerical models using different analysis methods, and those derived here by the linear relationship mentioned above from circulation analogs are gratifyingly similar. These estimates indicate that given the present observational error (about 12 m rms) and spatial resolution of the data, in the NH winter, the atmosphere seems to have 17-18 days of predictability before the initial difference reaches 95% of the saturation Revel (random error). In present models, the forecast error reaches the same 95% level at around ten and a half days. Since the climate mean as a forecast has considerably less error than a random forecast, from a forecaster's point of view it is more appropriate to use the climate mor as a reference level (71% of the saturation level). With the same conditions as above and using this alternative error reference level, the atmosphere might have a predictability of nine days, while the two models considered currently exhaust predictability at close to six days, leaving considerable room for improvement. Note that these atmospheric predictability estimates were obtained without considering a possible enhancement of predictability due to interactions with the slowly changing ocean and other geospheres. Hence, these estimates can be considered as lower limits to atmospheric predictability.

Comparing the predictability estimates gained from twin model experiments to those from observational data is a special, complex method of model verification. Keeping in mind the uncertainties in the observational studies, one can ascertain that the models produce quite similar error growth characteristics to those of the real atmosphere. Hence, the NWP models are quite good on the tirne and spatial scales for which they were designed. However, there are some indications that they probably could not be reliably used to answer the theoretical questions regarding the gain in predictability with very small initial errors or with very high spatial resolution. Some kind of dynamic-empirical approach based on the interactions between different scales of motion is required to enhance current knowledge on these topics.

Abstract

An empirical predictability study is presented based on 7OO-hPa Northern Hemispheric circulation analogs. A linear relationship between the initial root-mean-square difference of analog pairs and the time taken for the error to reach a certain limit value is used to extrapolate the predictability with initial errors considerably smaller than those in the present database. The relationship, first used in predictability experiments with the NMC numerical weather prediction (NWP) model, conforms to the experimental data in that the effor growth depends not only on the magnitude of the error but also, to a lesser extent, on the initial error.

Despite the fact that earlier error growth studies did not reflect this dependence on the initial error predictability results with two state-of-the-art numerical models using different analysis methods, and those derived here by the linear relationship mentioned above from circulation analogs are gratifyingly similar. These estimates indicate that given the present observational error (about 12 m rms) and spatial resolution of the data, in the NH winter, the atmosphere seems to have 17-18 days of predictability before the initial difference reaches 95% of the saturation Revel (random error). In present models, the forecast error reaches the same 95% level at around ten and a half days. Since the climate mean as a forecast has considerably less error than a random forecast, from a forecaster's point of view it is more appropriate to use the climate mor as a reference level (71% of the saturation level). With the same conditions as above and using this alternative error reference level, the atmosphere might have a predictability of nine days, while the two models considered currently exhaust predictability at close to six days, leaving considerable room for improvement. Note that these atmospheric predictability estimates were obtained without considering a possible enhancement of predictability due to interactions with the slowly changing ocean and other geospheres. Hence, these estimates can be considered as lower limits to atmospheric predictability.

Comparing the predictability estimates gained from twin model experiments to those from observational data is a special, complex method of model verification. Keeping in mind the uncertainties in the observational studies, one can ascertain that the models produce quite similar error growth characteristics to those of the real atmosphere. Hence, the NWP models are quite good on the tirne and spatial scales for which they were designed. However, there are some indications that they probably could not be reliably used to answer the theoretical questions regarding the gain in predictability with very small initial errors or with very high spatial resolution. Some kind of dynamic-empirical approach based on the interactions between different scales of motion is required to enhance current knowledge on these topics.

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