Search Results
You are looking at 1 - 7 of 7 items for
- Author or Editor: Risheng Wang x
- Refine by Access: All Content x
Abstract
A methodology is presented to make an optimal use of the global SST for the prediction of seasonal climates. First, the space–time extended principal component analysis was applied to the key SST forcing regions, such as the tropical Pacific and the Atlantic, to establish a low-dimensional phase space model. This allows a nonlinear prediction, in terms of analogs found in the nearest neighborhood of the state associated with the initial time of prediction. Second, the predicted results derived independently from those different SST forcing regions are then linearly combined using the best linear unbiased estimates based on all available verification periods under a cross-validation scheme. This enables optimal use of the predictive skills inherent to each of the key SST forcing regions for each climate zone. The proposed methodology is justified by the analysis of the origins of predictive skills for seasonal predictions based on SST predictors (the geographical distribution of the skill scores and their time changes). Application was made to the prediction of winter (December–January–February) surface air temperatures over North America, based on the observed monthly mean data from January 1949 to December 1996. Significant skill scores were found over most parts of North America. The superiority of nonlinear prediction was demonstrated. It is concluded that the low-dimensional phase space approach may be used as an effective tool for seasonal forecasting.
Abstract
A methodology is presented to make an optimal use of the global SST for the prediction of seasonal climates. First, the space–time extended principal component analysis was applied to the key SST forcing regions, such as the tropical Pacific and the Atlantic, to establish a low-dimensional phase space model. This allows a nonlinear prediction, in terms of analogs found in the nearest neighborhood of the state associated with the initial time of prediction. Second, the predicted results derived independently from those different SST forcing regions are then linearly combined using the best linear unbiased estimates based on all available verification periods under a cross-validation scheme. This enables optimal use of the predictive skills inherent to each of the key SST forcing regions for each climate zone. The proposed methodology is justified by the analysis of the origins of predictive skills for seasonal predictions based on SST predictors (the geographical distribution of the skill scores and their time changes). Application was made to the prediction of winter (December–January–February) surface air temperatures over North America, based on the observed monthly mean data from January 1949 to December 1996. Significant skill scores were found over most parts of North America. The superiority of nonlinear prediction was demonstrated. It is concluded that the low-dimensional phase space approach may be used as an effective tool for seasonal forecasting.
Abstract
The variability of El Niño–La Niña events was analyzed in a low-dimensional phase space, a concept derived from dynamic system theory. The space–time extended EOFs derived from the observed monthly mean SST field over tropical Pacific were used as the basis of the phase space that describes the time evolution of ENSO signals. It was shown that the essential features of the ENSO variability, such as the irregular oscillation, the phase locking to the annual cycle, and the interdecadal changes in its propagation and onset, can be effectively represented by a three-dimensional phase space. The typical El Niño–La Niña life cycle is four years with its mature phases in boreal winter. The intensity of the ENSO signal within one life cycle is closely linked to the frequency of its occurrence (onset). The interdecadal variability of the ENSO signals is characterized by both the intensity and the frequency of occurrence, displaying an irregularity with the gross feature comparable to the regime behavior and intermittency of some low-dimensional chaotic systems.
Abstract
The variability of El Niño–La Niña events was analyzed in a low-dimensional phase space, a concept derived from dynamic system theory. The space–time extended EOFs derived from the observed monthly mean SST field over tropical Pacific were used as the basis of the phase space that describes the time evolution of ENSO signals. It was shown that the essential features of the ENSO variability, such as the irregular oscillation, the phase locking to the annual cycle, and the interdecadal changes in its propagation and onset, can be effectively represented by a three-dimensional phase space. The typical El Niño–La Niña life cycle is four years with its mature phases in boreal winter. The intensity of the ENSO signal within one life cycle is closely linked to the frequency of its occurrence (onset). The interdecadal variability of the ENSO signals is characterized by both the intensity and the frequency of occurrence, displaying an irregularity with the gross feature comparable to the regime behavior and intermittency of some low-dimensional chaotic systems.
Abstract
Empirical orthogonal function analyses of the time-height series of monthly mean zonal wind in the stratosphere have been performed. Conventional EOF analysis on the time series reveals that the quasi-biennial oscillation (QBO) of the zonal wind is a quasi-regular oscillation with a period near 28 months, but the noisy structure of the first two EOFs, representing 57.18% and 36.24% of the variance of the time series, leads to little new insight concerning the dynamics of the QBO. A second type of analysis is performed by applying windows to the data and calculating the EOFs of the time development of the spatial structure. By using a window of around one-half of the period of the dominant oscillation, the EOF analysis reveals that the QBO is essentially a linear feature, with successive wind regimes propagating smoothly downwards with a spectral peak near 28 months; this oscillation has a very smooth phase portrait and cross-correlation analysis of the first two EOFs reveals that it is a quasi-linear feature with good predictability. Delays in the downward propagation of the easterly phase of the QBO are shown to be nonlinear, unpredictable features, represented by higher-order EOFS.
Abstract
Empirical orthogonal function analyses of the time-height series of monthly mean zonal wind in the stratosphere have been performed. Conventional EOF analysis on the time series reveals that the quasi-biennial oscillation (QBO) of the zonal wind is a quasi-regular oscillation with a period near 28 months, but the noisy structure of the first two EOFs, representing 57.18% and 36.24% of the variance of the time series, leads to little new insight concerning the dynamics of the QBO. A second type of analysis is performed by applying windows to the data and calculating the EOFs of the time development of the spatial structure. By using a window of around one-half of the period of the dominant oscillation, the EOF analysis reveals that the QBO is essentially a linear feature, with successive wind regimes propagating smoothly downwards with a spectral peak near 28 months; this oscillation has a very smooth phase portrait and cross-correlation analysis of the first two EOFs reveals that it is a quasi-linear feature with good predictability. Delays in the downward propagation of the easterly phase of the QBO are shown to be nonlinear, unpredictable features, represented by higher-order EOFS.
Abstract
Extended empirical orthogonal functions (EOFs) are used to define a phase space for the analysis of tropical stratospheric wind data, extending our previous study of the quasi-biennial oscillation (QBO) in several manners. First, the sensitivity of the analysis to the length of the window (w) is discussed in some detail. As w increases, the leading pair of EOFs become more concentrated on the period near 28 months; simultaneously, the signals contained in higher-order EOFs become more significant, with more clearly defined periodicities; however, for large w more EOFs are required to represent the same variance. There appear to be two stable regimes: when w is less than 20 months the first two EOFs describe a QBO with some irregularities in the onset of easterly wind regimes, whereas when w exceeds 30 months such irregularities are represented by the third and fourth EOFS. Second, the first pair of EOFs with w = 40 are regarded as representing a pure QBO signal, subject to variations in cycle length (ranging from 22 to 33 months) and amplitude but propagating smoothly. Its phase–space characteristics are examined in some detail; this oscillation is regarded as a limit cycle, subject to low-frequency variability, presumably due to fluctuations in the forcing mechanisms at work. No annual cycle is evident in its propagation in phase space. Third, departures from this pure QBO are examined. These are represented by higher-order signals with w = 40. EOFs 3 and 4 describe much of the irregularity in downward propagation of the wind regimes, with dominant periods in a broad band centered on 28 months; EOF 5 does not represent a propagating signal but some low-frequency variability (probably externally forced) in the vertical wind shear; E0Fs 6 and 7 are the subharmonics of the QBO; EOFs 8 and 9 represent the annual cycle.
Abstract
Extended empirical orthogonal functions (EOFs) are used to define a phase space for the analysis of tropical stratospheric wind data, extending our previous study of the quasi-biennial oscillation (QBO) in several manners. First, the sensitivity of the analysis to the length of the window (w) is discussed in some detail. As w increases, the leading pair of EOFs become more concentrated on the period near 28 months; simultaneously, the signals contained in higher-order EOFs become more significant, with more clearly defined periodicities; however, for large w more EOFs are required to represent the same variance. There appear to be two stable regimes: when w is less than 20 months the first two EOFs describe a QBO with some irregularities in the onset of easterly wind regimes, whereas when w exceeds 30 months such irregularities are represented by the third and fourth EOFS. Second, the first pair of EOFs with w = 40 are regarded as representing a pure QBO signal, subject to variations in cycle length (ranging from 22 to 33 months) and amplitude but propagating smoothly. Its phase–space characteristics are examined in some detail; this oscillation is regarded as a limit cycle, subject to low-frequency variability, presumably due to fluctuations in the forcing mechanisms at work. No annual cycle is evident in its propagation in phase space. Third, departures from this pure QBO are examined. These are represented by higher-order signals with w = 40. EOFs 3 and 4 describe much of the irregularity in downward propagation of the wind regimes, with dominant periods in a broad band centered on 28 months; EOF 5 does not represent a propagating signal but some low-frequency variability (probably externally forced) in the vertical wind shear; E0Fs 6 and 7 are the subharmonics of the QBO; EOFs 8 and 9 represent the annual cycle.
Abstract
Empirical orthogonal function (EOF) analyses are performed of time–height series of zonal and meridional winds and of cumulonimbus heating and drying in the Tropics. The data are from a rawinsonde array in the western Pacific located between the equator and 10°S during the intensive observation period of the Tropical Ocean Global Atmosphere Coupled Ocean–Atmosphere Response Experiment (TOGA COARE). The EOF analyses are performed by applying a window of 20 days to the data and thus calculating the EOFs of the time development of the vertical structure.
The wind time series is found to be well represented by two pairs of EOFs, each representing an oscillation. The first oscillation has a period of approximately 40 days, is predominantly in the zonal wind component, and has a first internal mode vertical structure with westerly anomalies in the lower troposphere corresponding to easterly perturbations in the upper troposphere. This pair describes 48.3% of the variance. A second EOF pair in the wind is a zonal variation that occurs predominantly in the upper troposphere. It has a period of approximately 24 days and describes 13.9% of the variance.
The heating–drying series is described by a dominant oscillation of period 40 days representing 41% of the variance. The structure is maximum in the middle troposphere and is associated with the same physical phenomeon as the dominant (u, υ) oscillation. The second EOF pair for heating–drying has a period of 13 days, so there is a large time separation in periodiocities for heating–drying compared to that for winds. The second (13 day) oscillation in heating–drying has the same vertical structure as the dominant (40 day) oscillation.
Abstract
Empirical orthogonal function (EOF) analyses are performed of time–height series of zonal and meridional winds and of cumulonimbus heating and drying in the Tropics. The data are from a rawinsonde array in the western Pacific located between the equator and 10°S during the intensive observation period of the Tropical Ocean Global Atmosphere Coupled Ocean–Atmosphere Response Experiment (TOGA COARE). The EOF analyses are performed by applying a window of 20 days to the data and thus calculating the EOFs of the time development of the vertical structure.
The wind time series is found to be well represented by two pairs of EOFs, each representing an oscillation. The first oscillation has a period of approximately 40 days, is predominantly in the zonal wind component, and has a first internal mode vertical structure with westerly anomalies in the lower troposphere corresponding to easterly perturbations in the upper troposphere. This pair describes 48.3% of the variance. A second EOF pair in the wind is a zonal variation that occurs predominantly in the upper troposphere. It has a period of approximately 24 days and describes 13.9% of the variance.
The heating–drying series is described by a dominant oscillation of period 40 days representing 41% of the variance. The structure is maximum in the middle troposphere and is associated with the same physical phenomeon as the dominant (u, υ) oscillation. The second EOF pair for heating–drying has a period of 13 days, so there is a large time separation in periodiocities for heating–drying compared to that for winds. The second (13 day) oscillation in heating–drying has the same vertical structure as the dominant (40 day) oscillation.
Abstract
The statistical model proposed by Vautard et al. is applied to the seasonal prediction of surface air temperatures over North America (Canada and the United States). This model is based on sea surface temperature predictors filtered by multichannel singular spectrum analysis (MSSA), which is equivalent here to a nonseasonal version of extended EOF analysis. Several versions of the MSSA model are proposed. The most successful one is based on a two-step procedure consisting in a prior prediction of filtered sea surface temperatures followed by a predictand specification stage.
The MSSA model is compared with the recent prediction technique based on canonical correlation analysis (CCA). The former model turns out, in this application, to be more skillful in most seasons than the latter. The differences are, however, marginal. The authors argue that these differences are due to the nonseasonal nature of the MSSA model and to overfitting problems inherent to CCA. Another advantage of the MSSA model relative to CCA is the possibility of easily transforming deterministic continuous forecasts into probabilistic categorical forecasts.
The geographical distribution of prediction skill across North America is studied. Canada turns out to be the country where skill is most significant. During winter, high skill values are also found over the southeastern United States.
Abstract
The statistical model proposed by Vautard et al. is applied to the seasonal prediction of surface air temperatures over North America (Canada and the United States). This model is based on sea surface temperature predictors filtered by multichannel singular spectrum analysis (MSSA), which is equivalent here to a nonseasonal version of extended EOF analysis. Several versions of the MSSA model are proposed. The most successful one is based on a two-step procedure consisting in a prior prediction of filtered sea surface temperatures followed by a predictand specification stage.
The MSSA model is compared with the recent prediction technique based on canonical correlation analysis (CCA). The former model turns out, in this application, to be more skillful in most seasons than the latter. The differences are, however, marginal. The authors argue that these differences are due to the nonseasonal nature of the MSSA model and to overfitting problems inherent to CCA. Another advantage of the MSSA model relative to CCA is the possibility of easily transforming deterministic continuous forecasts into probabilistic categorical forecasts.
The geographical distribution of prediction skill across North America is studied. Canada turns out to be the country where skill is most significant. During winter, high skill values are also found over the southeastern United States.
Abstract
Partitioning evapotranspiration (ET) into soil evaporation E and plant transpiration T is important, but it is still a theoretical and technical challenge. The isotopic technique is considered to be an effective method, but it is difficult to quantify the isotopic composition of transpiration δ T and evaporation δ E directly and continuously; few previous studies determined δ T successfully under a non-steady state (NSS). Here, multiple methods were used to partition ET in a maize field and a new flow-through chamber system was refined to provide direct and continuous measurement of δ T and δ E . An eddy covariance and lysimeter (EC-L)-based method and two isotope-based methods [isotope combined with the Craig–Gordon model (Iso-CG) and isotope using chamber measurement (Iso-M)] were applied to partition ET. Results showed the transpiration fraction F T in Iso-CG was consistent with EC-L at both diurnal and growing season time scales, but F T calculated by Iso-M was less than Iso-CG and EC-L. The chamber system method presented here to determine δ T under NSS and isotope steady state (ISS) was robust, but there could be some deviation in measuring δ E . The F T varied from 52% to 91%, with a mean of 78% during the entire growing season, and it was well described by a function of LAI, with a nonlinear relationship of F T = 0.71LAI0.14. The results demonstrated the feasibility of the isotope-based chamber system to partition ET. This technique and its further development may enable field ET partitioning accurately and continuously and improve understanding of water cycling through the soil–plant–atmosphere continuum.
Abstract
Partitioning evapotranspiration (ET) into soil evaporation E and plant transpiration T is important, but it is still a theoretical and technical challenge. The isotopic technique is considered to be an effective method, but it is difficult to quantify the isotopic composition of transpiration δ T and evaporation δ E directly and continuously; few previous studies determined δ T successfully under a non-steady state (NSS). Here, multiple methods were used to partition ET in a maize field and a new flow-through chamber system was refined to provide direct and continuous measurement of δ T and δ E . An eddy covariance and lysimeter (EC-L)-based method and two isotope-based methods [isotope combined with the Craig–Gordon model (Iso-CG) and isotope using chamber measurement (Iso-M)] were applied to partition ET. Results showed the transpiration fraction F T in Iso-CG was consistent with EC-L at both diurnal and growing season time scales, but F T calculated by Iso-M was less than Iso-CG and EC-L. The chamber system method presented here to determine δ T under NSS and isotope steady state (ISS) was robust, but there could be some deviation in measuring δ E . The F T varied from 52% to 91%, with a mean of 78% during the entire growing season, and it was well described by a function of LAI, with a nonlinear relationship of F T = 0.71LAI0.14. The results demonstrated the feasibility of the isotope-based chamber system to partition ET. This technique and its further development may enable field ET partitioning accurately and continuously and improve understanding of water cycling through the soil–plant–atmosphere continuum.
