A statistical dynamics model of the marine ecosystem and its application in Jiaozhou Bay

Models of marine ecosystem dynamics play an important role in revealing the evolution mechanisms of marine ecosystems and in forecasting their future changes. Most traditional ecological dynamics models are established based on basic physical and biological laws, and have obvious dynamic characteristics and ecological significance. However, they are not flexible enough for the variability of environment conditions and ecological processes found in offshore marine areas, where it is often difficult to obtain parameters for the model, and the precision of the model is often low. In this paper, a new modeling method is introduced, which aims to establish an evolution model of marine ecosystems by coupling statistics with differential dynamics. Firstly, we outline the basic concept and method of inverse modeling of marine ecosystems. Then we set up a statistical dynamics model of marine ecosystems evolution according to annual ecological observation data from Jiaozhou Bay. This was done under the forcing conditions of sea surface temperature and surface irradiance and considering the state variables of phytoplankton, zooplankton and nutrients. This model is dynamic, makes the best of field observation data, and the average predicted precision can reach 90% or higher. A simpler model can be easily obtained through eliminating the terms with smaller contributions according to the weight coefficients of model differential items. The method proposed in this paper avoids the difficulties of obtaining and optimizing parameters, which exist in traditional research, and it provides a new path for research of marine ecological dynamics.

ON THE MECHANISM OF TURBULENT COHERENT STRUCTURE (III)──A STATISTICAL AND DYNAMICALMODEL OF COHERENT STRUCTURE AND ITSHEAT TRANSFER MECHANISM

Following Tsai & Ma[1] and Tsai & Liu[2], a statistical and dynamical near-wall turbulent coherent structural model with separate consideration of two different portions:locally generated and upstream-transported large eddies has been established.With this model, heat transfer in a fully developed open channel in the absence of pressure gradient is numerically simulated. Database of fluctuations of velocity and temperature has also been set. Numerical analysis shows the existence of high-low temperature streak caused by near-wall coherent structure and its swing in the lateral direction.Numerical results are in accordance with the computations and experimental results of other researchers.

A Hybrid Statistical-Dynamical Downscaling of Air Temperature over Scandinavia Using the WRF Model

An accurate simulation of air temperature at local scales is crucial for the vast majority of weather and climate applications.In this work,a hybrid statistical–dynamical downscaling method and a high-resolution dynamical-only downscaling method are applied to daily mean,minimum and maximum air temperatures to investigate the quality of localscale estimates produced by downscaling.These two downscaling approaches are evaluated using station observation data obtained from the Finnish Meteorological Institute over a near-coastal region of western Finland.The dynamical downscaling is performed with the Weather Research and Forecasting(WRF)model,and the statistical downscaling method implemented is the Cumulative Distribution Function-transform(CDF-t).The CDF-t is trained using 20 years of WRF-downscaled Climate Forecast System Reanalysis data over the region at a 3-km spatial resolution for the central month of each season.The performance of the two methods is assessed qualitatively,by inspection of quantile-quantile plots,and quantitatively,through the Cramer-von Mises,mean absolute error,and root-mean-square error diagnostics.The hybrid approach is found to provide significantly more skillful forecasts of the observed daily mean and maximum air temperatures than those of the dynamical-only downscaling(for all seasons).The hybrid method proves to be less computationally expensive,and also to give more skillful temperature forecasts(at least for the Finnish near-coastal region).

Verification and Improvement of the Ability of CFSv2 to Predict the Antarctic Oscillation in Boreal Spring

The boreal spring Antarctic Oscillation(AAO)has a significant impact on the spring and summer climate in China.This study evaluates the capability of the NCEP's Climate Forecast System,version 2(CFSv2),in predicting the boreal spring AAO for the period 1983-2015.The results indicate that CFSv2 has poor skill in predicting the spring AAO,failing to predict the zonally symmetric spatial pattern of the AAO,with an insignificant correlation of 0.02 between the predicted and observed AAO Index(AAOI).Considering the interannual increment approach can amplify the prediction signals,we firstly establish a dynamical-statistical model to improve the interannual increment of the AAOI(DY AAOI),with two predictors of CFSv2-forecasted concurrent spring sea surface temperatures and observed preceding autumn sea ice.This dynamical-statistical model demonstrates good capability in predicting DY AAOI,with a significant correlation coeffcient of 0.58 between the observation and prediction during 1983-2015 in the two-year-out cross-validation.Then,we obtain an improved AAOI by adding the improved DY AAOI to the preceding observed AAOI.The improved AAOI shows a significant correlation coeffcient of 0.45 with the observed AAOI during 1983-2015.Moreover,the unrealistic atmospheric response to March-April-May sea ice in CFSv2 may be the possible cause for the failure of CFSv2 to predict the AAO.This study gives new clues regarding AAO prediction and short-term climate prediction.