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- Author or Editor: Wansuo Duan x
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Abstract
Conditional nonlinear optimal perturbation (CNOP) is the initial perturbation that satisfies a certain physical constraint and causes the largest nonlinear evolution at prediction time. To yield mutually independent initial perturbations in ensemble forecasts, orthogonal CNOPs are developed. Orthogonal CNOPs are then applied to a Lorenz-96 model to generate initial perturbations for ensemble forecasting, as compared with orthogonal singular vectors (SVs). When the initial analysis errors are fast growing, the ensemble forecasts generated by orthogonal CNOPs of the control forecasts perform much more skillfully. Nevertheless, for slow-growing initial analysis errors, the ensemble forecasts generated by orthogonal SVs achieve higher skill when the ensemble initial perturbations are large, whereas the ensemble forecasts generated by orthogonal CNOPs achieve almost the same forecast skill as those generated by orthogonal SVs when the ensemble initial perturbations are sufficiently small. The initial analysis errors that possess much faster growth behavior are easily influenced by nonlinearity, and extreme events (extreme here refers to strong), because of strong nonlinear instability, may be much more likely to cause fast growth of initial analysis errors. Therefore, the ensemble forecasts generated by orthogonal CNOPs may have higher skill than those generated by orthogonal SVs for extreme events; in particular, the ensemble forecasts generated by orthogonal CNOPs, compared with those generated by orthogonal SVs, require a much smaller number of ensemble members to achieve high skill. Therefore, orthogonal CNOPs may provide another useful technique to generate initial perturbations for ensemble forecasting.
Abstract
Conditional nonlinear optimal perturbation (CNOP) is the initial perturbation that satisfies a certain physical constraint and causes the largest nonlinear evolution at prediction time. To yield mutually independent initial perturbations in ensemble forecasts, orthogonal CNOPs are developed. Orthogonal CNOPs are then applied to a Lorenz-96 model to generate initial perturbations for ensemble forecasting, as compared with orthogonal singular vectors (SVs). When the initial analysis errors are fast growing, the ensemble forecasts generated by orthogonal CNOPs of the control forecasts perform much more skillfully. Nevertheless, for slow-growing initial analysis errors, the ensemble forecasts generated by orthogonal SVs achieve higher skill when the ensemble initial perturbations are large, whereas the ensemble forecasts generated by orthogonal CNOPs achieve almost the same forecast skill as those generated by orthogonal SVs when the ensemble initial perturbations are sufficiently small. The initial analysis errors that possess much faster growth behavior are easily influenced by nonlinearity, and extreme events (extreme here refers to strong), because of strong nonlinear instability, may be much more likely to cause fast growth of initial analysis errors. Therefore, the ensemble forecasts generated by orthogonal CNOPs may have higher skill than those generated by orthogonal SVs for extreme events; in particular, the ensemble forecasts generated by orthogonal CNOPs, compared with those generated by orthogonal SVs, require a much smaller number of ensemble members to achieve high skill. Therefore, orthogonal CNOPs may provide another useful technique to generate initial perturbations for ensemble forecasting.
Abstract
Nonlinear forcing singular vector (NFSV)-based assimilation is adopted to determine the model tendency errors that represent the combined effect of different kinds of model errors; then, an NFSV-tendency error forecast model is formulated. This error forecast model is coupled with an intermediate complex model (ICM) and makes the ICM output closer to the observations; finally, an NFSV-ICM forecast model for ENSO is constructed. The competing aspect of the NFSV-ICM is to consider not only model errors but also the interaction between model errors and initial errors because of the mathematical nature of the NFSV-tendency errors. Based on the prediction experiments for tropical SSTAs during either the training period (1960–96; i.e., when the NFSV-ICM is formulated) or the cross-validation period (1997–2016), the NFSV-ICM is determined to have a much higher forecast skill in predicting ENSO that, specifically, extends the skillful predictions of ENSO from a lead time of 6 months in the original ICM to a lead time of 12 months. The higher skill of the NFSV-ICM is especially reflected in the predictions of SSTAs in the central and western Pacific. For the well-known spring predictability barrier (SPB) phenomenon that greatly limits ENSO forecasting skill, the NFSV-ICM also shows great abilities in suppressing its negative effect on ENSO predictions. Although the NFSV-ICM is presently only involved with the NFSV-related assimilation of SSTs, it has shown its usefulness in predicting ENSO. It is clear that the NFSV-based assimilation approach is effective in dealing with the effect of model errors on ENSO forecasts.
Abstract
Nonlinear forcing singular vector (NFSV)-based assimilation is adopted to determine the model tendency errors that represent the combined effect of different kinds of model errors; then, an NFSV-tendency error forecast model is formulated. This error forecast model is coupled with an intermediate complex model (ICM) and makes the ICM output closer to the observations; finally, an NFSV-ICM forecast model for ENSO is constructed. The competing aspect of the NFSV-ICM is to consider not only model errors but also the interaction between model errors and initial errors because of the mathematical nature of the NFSV-tendency errors. Based on the prediction experiments for tropical SSTAs during either the training period (1960–96; i.e., when the NFSV-ICM is formulated) or the cross-validation period (1997–2016), the NFSV-ICM is determined to have a much higher forecast skill in predicting ENSO that, specifically, extends the skillful predictions of ENSO from a lead time of 6 months in the original ICM to a lead time of 12 months. The higher skill of the NFSV-ICM is especially reflected in the predictions of SSTAs in the central and western Pacific. For the well-known spring predictability barrier (SPB) phenomenon that greatly limits ENSO forecasting skill, the NFSV-ICM also shows great abilities in suppressing its negative effect on ENSO predictions. Although the NFSV-ICM is presently only involved with the NFSV-related assimilation of SSTs, it has shown its usefulness in predicting ENSO. It is clear that the NFSV-based assimilation approach is effective in dealing with the effect of model errors on ENSO forecasts.
Abstract
In this paper, we investigate the relationship between the El Niño–Southern Oscillation (ENSO) spring persistence barrier (PB) and predictability barrier (PD) and apply it to explain the interdecadal modulation of ENSO prediction skill using the anomaly correlation coefficient (ACC). Previous studies showed that a longer persistence (i.e., autocorrelation) tends to produce a higher prediction skill. Using the recharge oscillator model of ENSO, both analytical and numerical solutions suggest that the predictability (i.e., ACC) is related to the persistence of sea surface temperature (SST) and cross correlation between SST and subsurface ocean heat content in the tropical Pacific. In particular, a larger damping rate in SST anomalies will lead to a lower persistence and ACC and a stronger PD. However, a shortened ENSO period, which controls the cross correlation, will lead to a lower persistence but a higher ACC associated with a weaker PD. Finally, we apply our solutions to observations and suggest that a higher ACC associated with a weaker PD after 1960 is caused by the shortened ENSO period.
Abstract
In this paper, we investigate the relationship between the El Niño–Southern Oscillation (ENSO) spring persistence barrier (PB) and predictability barrier (PD) and apply it to explain the interdecadal modulation of ENSO prediction skill using the anomaly correlation coefficient (ACC). Previous studies showed that a longer persistence (i.e., autocorrelation) tends to produce a higher prediction skill. Using the recharge oscillator model of ENSO, both analytical and numerical solutions suggest that the predictability (i.e., ACC) is related to the persistence of sea surface temperature (SST) and cross correlation between SST and subsurface ocean heat content in the tropical Pacific. In particular, a larger damping rate in SST anomalies will lead to a lower persistence and ACC and a stronger PD. However, a shortened ENSO period, which controls the cross correlation, will lead to a lower persistence but a higher ACC associated with a weaker PD. Finally, we apply our solutions to observations and suggest that a higher ACC associated with a weaker PD after 1960 is caused by the shortened ENSO period.
Abstract
Within the framework of the Zebiak–Cane model, the approach of conditional nonlinear optimal perturbation (CNOP) is used to study the effect of model parameter errors on El Niño–Southern Oscillation (ENSO) predictability. The optimal model parameter errors are obtained within a reasonable error bound (i.e., CNOP-P errors), which have the largest effect on the results of El Niño predictions. The resultant prediction errors were investigated in depth. The CNOP-P errors do not cause a noticeable prediction error of the sea surface temperature anomaly averaged over the Niño-3 region and do not show an obvious season-dependent evolution of the prediction errors. Consequently, the CNOP-P errors do not cause a significant spring predictability barrier (SPB) for El Niño events. In contrast, the initial errors that have the largest effect on the results of the predictions (i.e., the CNOP-I errors) show a season-dependent evolution, with the largest error increase in spring, and also cause a large prediction error, thereby generating a significant SPB. The initial errors play a more important role than the parameter errors in generating a significant SPB for El Niño events. To further validate this result, the authors investigated the situation in which CNOP-I and CNOP-P errors are simultaneously superimposed in the model, which may be a more credible approach because the initial errors and model parameter errors coexist under realistic predictions. The combined mode of CNOP-I and CNOP-P errors shows a similar season-dependent evolution to that of CNOP-I errors and yields a large prediction error, thereby inducing a significant SPB. The inference, therefore, is that initial errors play a more important role than model parameter errors in generating a significant SPB for El Niño predictions of the Zebiak–Cane model. This result helps to clarify the roles of the initial error and parameter error in the development of an SPB, and highlights the role of initial errors, which demonstrates that the SPB could be markedly reduced by improving the initial conditions. The results provide a theoretical basis for improving data assimilation in ENSO predictions.
Abstract
Within the framework of the Zebiak–Cane model, the approach of conditional nonlinear optimal perturbation (CNOP) is used to study the effect of model parameter errors on El Niño–Southern Oscillation (ENSO) predictability. The optimal model parameter errors are obtained within a reasonable error bound (i.e., CNOP-P errors), which have the largest effect on the results of El Niño predictions. The resultant prediction errors were investigated in depth. The CNOP-P errors do not cause a noticeable prediction error of the sea surface temperature anomaly averaged over the Niño-3 region and do not show an obvious season-dependent evolution of the prediction errors. Consequently, the CNOP-P errors do not cause a significant spring predictability barrier (SPB) for El Niño events. In contrast, the initial errors that have the largest effect on the results of the predictions (i.e., the CNOP-I errors) show a season-dependent evolution, with the largest error increase in spring, and also cause a large prediction error, thereby generating a significant SPB. The initial errors play a more important role than the parameter errors in generating a significant SPB for El Niño events. To further validate this result, the authors investigated the situation in which CNOP-I and CNOP-P errors are simultaneously superimposed in the model, which may be a more credible approach because the initial errors and model parameter errors coexist under realistic predictions. The combined mode of CNOP-I and CNOP-P errors shows a similar season-dependent evolution to that of CNOP-I errors and yields a large prediction error, thereby inducing a significant SPB. The inference, therefore, is that initial errors play a more important role than model parameter errors in generating a significant SPB for El Niño predictions of the Zebiak–Cane model. This result helps to clarify the roles of the initial error and parameter error in the development of an SPB, and highlights the role of initial errors, which demonstrates that the SPB could be markedly reduced by improving the initial conditions. The results provide a theoretical basis for improving data assimilation in ENSO predictions.
Abstract
In this paper, a new nonlinear forcing singular vector (NFSV) approach is proposed to provide mutually independent optimally combined modes of initial perturbations and model perturbations (C-NFSVs) in ensemble forecasts. The C-NFSVs are a group of optimally growing structures that take into account the impact of the interaction between the initial errors and the model errors effectively, generalizing the original NFSV for simulations of the impact of the model errors. The C-NFSVs method is tested in the context of the Lorenz-96 model to demonstrate its potential to improve ensemble forecast skills. This method is compared with the orthogonal conditional nonlinear optimal perturbations (O-CNOPs) method for estimating only the initial uncertainties and the orthogonal NFSVs (O-NFSVs) for estimating only the model uncertainties. The results demonstrate that when both the initial perturbations and model perturbations are introduced in the forecasting system, the C-NFSVs are much more capable of achieving higher ensemble forecasting skills. The use of a deep learning approach as a remedy for the expensive computational costs of the C-NFSVs is evaluated. The results show that learning the impact of the C-NFSVs on the ensemble provides a useful and efficient alternative for the operational implementation of C-NFSVs in forecasting suites dealing with the combined effects of the initial errors and the model errors.
Significance Statement
A new ensemble forecasting method for dealing with combined effects of initial errors and model errors, i.e., the C-NFSVs, is proposed, which is an extension of the NFSV approach for simulating the model error effects in ensemble forecasts. The C-NFSVs provide mutually independent optimally combined modes of initial perturbations and model perturbations. This new method is tested for generating ensemble forecasts in the context of the Lorenz-96 model, and there are indications that the optimally growing structures may provide reliable ensemble forecasts. Furthermore, it is found that a hybrid dynamical–deep learning approach could be a potential avenue for real-time ensemble forecasting systems when perturbations combine the impact of the initial and the model errors.
Abstract
In this paper, a new nonlinear forcing singular vector (NFSV) approach is proposed to provide mutually independent optimally combined modes of initial perturbations and model perturbations (C-NFSVs) in ensemble forecasts. The C-NFSVs are a group of optimally growing structures that take into account the impact of the interaction between the initial errors and the model errors effectively, generalizing the original NFSV for simulations of the impact of the model errors. The C-NFSVs method is tested in the context of the Lorenz-96 model to demonstrate its potential to improve ensemble forecast skills. This method is compared with the orthogonal conditional nonlinear optimal perturbations (O-CNOPs) method for estimating only the initial uncertainties and the orthogonal NFSVs (O-NFSVs) for estimating only the model uncertainties. The results demonstrate that when both the initial perturbations and model perturbations are introduced in the forecasting system, the C-NFSVs are much more capable of achieving higher ensemble forecasting skills. The use of a deep learning approach as a remedy for the expensive computational costs of the C-NFSVs is evaluated. The results show that learning the impact of the C-NFSVs on the ensemble provides a useful and efficient alternative for the operational implementation of C-NFSVs in forecasting suites dealing with the combined effects of the initial errors and the model errors.
Significance Statement
A new ensemble forecasting method for dealing with combined effects of initial errors and model errors, i.e., the C-NFSVs, is proposed, which is an extension of the NFSV approach for simulating the model error effects in ensemble forecasts. The C-NFSVs provide mutually independent optimally combined modes of initial perturbations and model perturbations. This new method is tested for generating ensemble forecasts in the context of the Lorenz-96 model, and there are indications that the optimally growing structures may provide reliable ensemble forecasts. Furthermore, it is found that a hybrid dynamical–deep learning approach could be a potential avenue for real-time ensemble forecasting systems when perturbations combine the impact of the initial and the model errors.
Abstract
We used the conditional nonlinear optimal perturbation (CNOP) approach to investigate the most sensitive initial error of sea surface height anomaly (SSHA) forecasts by using a two-layer quasigeostrophic model and revealed the importance of mesoscale eddies in initialization of the SSHA forecasts. Then, the CNOP-type initial errors for individual mesoscale eddies were calculated, revealing that the errors tend to occur in locations where the eddies present a clear high- to low-velocity gradient along the eddy rotation and the errors often have a shear SSHA structure present. Physically, we interpreted the rationality of the particular location and shear structure of the CNOP-type errors by barotropic instability from the perspective of the Lagrange expression of fluid motions. Numerically, we examined the sensitivity of the CNOP-type errors by using observing system simulation experiments (OSSEs). We concluded that if additional observations are preferentially implemented in the location where CNOP-type errors occur, especially with a particular array indicated by their shear structure, the forecast ability of the SSHA can be significantly improved. These results provide scientific guidance for the target observation of mesoscale eddies and therefore are very instructive for improving ocean state SSHA forecasts.
Abstract
We used the conditional nonlinear optimal perturbation (CNOP) approach to investigate the most sensitive initial error of sea surface height anomaly (SSHA) forecasts by using a two-layer quasigeostrophic model and revealed the importance of mesoscale eddies in initialization of the SSHA forecasts. Then, the CNOP-type initial errors for individual mesoscale eddies were calculated, revealing that the errors tend to occur in locations where the eddies present a clear high- to low-velocity gradient along the eddy rotation and the errors often have a shear SSHA structure present. Physically, we interpreted the rationality of the particular location and shear structure of the CNOP-type errors by barotropic instability from the perspective of the Lagrange expression of fluid motions. Numerically, we examined the sensitivity of the CNOP-type errors by using observing system simulation experiments (OSSEs). We concluded that if additional observations are preferentially implemented in the location where CNOP-type errors occur, especially with a particular array indicated by their shear structure, the forecast ability of the SSHA can be significantly improved. These results provide scientific guidance for the target observation of mesoscale eddies and therefore are very instructive for improving ocean state SSHA forecasts.
Abstract
The orthogonal conditional nonlinear optimal perturbations (O-CNOPs) approach for measuring initial uncertainties is applied to the Weather Research and Forecasting (WRF) Model to provide skillful forecasts of tropical cyclone (TC) tracks. The hindcasts for 10 TCs selected from 2005 to 2020 show that the ensembles generated by the O-CNOPs have a greater probability of capturing the true TC tracks, and the corresponding ensemble forecasts significantly outperform the forecasts made by the singular vectors, bred vectors, and random perturbations in terms of both deterministic and probabilistic skills. In particular, for two unusual TCs, Megi (2010) and Tembin (2012), the ensembles generated by the O-CNOPs successfully reproduce the sharp northward-turning track in the former and the counterclockwise loop track in the latter, while the ensembles generated by the other methods fail to do so. Moreover, additional attempts are performed on the real-time forecasts of TCs In-Fa (2021) and Hinnamnor (2022), and it is shown that O-CNOPs are very useful for improving the accuracy of real-time TC track forecasts. Therefore, O-CNOPs, together with the WRF Model, could provide a new platform for the ensemble forecasting of TC tracks with much higher skill.
Abstract
The orthogonal conditional nonlinear optimal perturbations (O-CNOPs) approach for measuring initial uncertainties is applied to the Weather Research and Forecasting (WRF) Model to provide skillful forecasts of tropical cyclone (TC) tracks. The hindcasts for 10 TCs selected from 2005 to 2020 show that the ensembles generated by the O-CNOPs have a greater probability of capturing the true TC tracks, and the corresponding ensemble forecasts significantly outperform the forecasts made by the singular vectors, bred vectors, and random perturbations in terms of both deterministic and probabilistic skills. In particular, for two unusual TCs, Megi (2010) and Tembin (2012), the ensembles generated by the O-CNOPs successfully reproduce the sharp northward-turning track in the former and the counterclockwise loop track in the latter, while the ensembles generated by the other methods fail to do so. Moreover, additional attempts are performed on the real-time forecasts of TCs In-Fa (2021) and Hinnamnor (2022), and it is shown that O-CNOPs are very useful for improving the accuracy of real-time TC track forecasts. Therefore, O-CNOPs, together with the WRF Model, could provide a new platform for the ensemble forecasting of TC tracks with much higher skill.
Abstract
The western-central equatorial Pacific (WCEP) zonal wind affects El Niño–Southern Oscillation (ENSO) by involving a series of multiscale air–sea interactions. Its interannual variation contributes the most to ENSO amplitude. Thus, understanding the predictability of the WCEP interannual wind is of great importance for better predictions of ENSO. Here, we show that the North Pacific Oscillation (NPO) and the South Pacific Oscillation (SPO) alternate in fueling this interannual wind from late boreal winter to austral winter in the presence of background trade winds in different hemispheres. During the boreal winter–spring, the NPO registers footprints in the tropics by benefiting from the Pacific meridional mode and modulating the northwestern Pacific intertropical convergence zone (NITCZ). However, as austral winter approaches, the SPO takes over the role of the NPO in maintaining the anomalous NITCZ. Moreover, the interannual wind is further driven to the east in the positive phase of the SPO, by intensified central-eastern equatorial Pacific convection resulting from tropical–extratropical heat flux adjustments. A reconstructed WCEP interannual wind index involving only the NPO and the SPO possesses a long lead time for ENSO prediction of nearly one year. These two extratropical boosters enhance the viability of equatorial Pacific zonal wind anomalies associated with the large growth rate of ENSO, and the one in the winter hemisphere seems to be more efficient in forcing the tropics. Our result further indicates that the NPO benefits a long-lead prediction of the WCEP interannual wind and ENSO, while the SPO is the dominant extratropical predictor of ENSO amplitude.
Significance Statement
ENSO is closely linked to the interannual variability of equatorial Pacific zonal wind, and ENSO prediction is impeded by the weak predictability of the wind. We have found that the North Pacific Oscillation and the South Pacific Oscillation take turns in affecting the interannual variability of the zonal wind from the late boreal winter to austral winter, and the winter hemisphere extratropical booster is more efficient in modulating tropical convection and the associated surface winds. An estimated zonal wind index constructed by the two extratropical precursors possesses a long lead time for ENSO prediction. Our result provides useful information for better predicting ENSO by further considering winter hemisphere extratropical climate variability.
Abstract
The western-central equatorial Pacific (WCEP) zonal wind affects El Niño–Southern Oscillation (ENSO) by involving a series of multiscale air–sea interactions. Its interannual variation contributes the most to ENSO amplitude. Thus, understanding the predictability of the WCEP interannual wind is of great importance for better predictions of ENSO. Here, we show that the North Pacific Oscillation (NPO) and the South Pacific Oscillation (SPO) alternate in fueling this interannual wind from late boreal winter to austral winter in the presence of background trade winds in different hemispheres. During the boreal winter–spring, the NPO registers footprints in the tropics by benefiting from the Pacific meridional mode and modulating the northwestern Pacific intertropical convergence zone (NITCZ). However, as austral winter approaches, the SPO takes over the role of the NPO in maintaining the anomalous NITCZ. Moreover, the interannual wind is further driven to the east in the positive phase of the SPO, by intensified central-eastern equatorial Pacific convection resulting from tropical–extratropical heat flux adjustments. A reconstructed WCEP interannual wind index involving only the NPO and the SPO possesses a long lead time for ENSO prediction of nearly one year. These two extratropical boosters enhance the viability of equatorial Pacific zonal wind anomalies associated with the large growth rate of ENSO, and the one in the winter hemisphere seems to be more efficient in forcing the tropics. Our result further indicates that the NPO benefits a long-lead prediction of the WCEP interannual wind and ENSO, while the SPO is the dominant extratropical predictor of ENSO amplitude.
Significance Statement
ENSO is closely linked to the interannual variability of equatorial Pacific zonal wind, and ENSO prediction is impeded by the weak predictability of the wind. We have found that the North Pacific Oscillation and the South Pacific Oscillation take turns in affecting the interannual variability of the zonal wind from the late boreal winter to austral winter, and the winter hemisphere extratropical booster is more efficient in modulating tropical convection and the associated surface winds. An estimated zonal wind index constructed by the two extratropical precursors possesses a long lead time for ENSO prediction. Our result provides useful information for better predicting ENSO by further considering winter hemisphere extratropical climate variability.
Abstract
Based on 36-yr hindcasts from the fifth-generation seasonal forecast system of the European Centre for Medium-Range Weather Forecasts (SEAS5), the most predictable patterns of the wintertime 2-m air temperature (T2m) in the extratropical Northern Hemisphere are extracted via the maximum signal-to-noise (MSN) empirical orthogonal function (EOF) analysis, and their associated predictability sources are identified. The MSN EOF1 captures the warming trend that amplifies over the Arctic but misses the associated warm Arctic–cold continent pattern. The MSN EOF2 delineates a wavelike T2m pattern over the Pacific–North America region, which is rooted in the tropical forcing of the eastern Pacific-type El Niño–Southern Oscillation (ENSO). The MSN EOF3 shows a wavelike T2m pattern over the Pacific–North America region, which has an approximately 90° phase difference from that associated with MSN EOF2, and a loading center over midlatitude Eurasia. Its sources of predictability include the central Pacific-type ENSO and Eurasian snow cover. The MSN EOF4 reflects T2m variability surrounding the Tibetan Plateau, which is plausibly linked to the remote forcing of the Arctic sea ice. The information on the leading predictable patterns and their sources of predictability is further used to develop a calibration scheme to improve the prediction skill of T2m. The calibrated prediction skill in terms of the anomaly correlation coefficient improves significantly over midlatitude Eurasia in a leave-one-out cross-validation, implying a possible way to improve the wintertime T2m prediction in the SEAS5.
Abstract
Based on 36-yr hindcasts from the fifth-generation seasonal forecast system of the European Centre for Medium-Range Weather Forecasts (SEAS5), the most predictable patterns of the wintertime 2-m air temperature (T2m) in the extratropical Northern Hemisphere are extracted via the maximum signal-to-noise (MSN) empirical orthogonal function (EOF) analysis, and their associated predictability sources are identified. The MSN EOF1 captures the warming trend that amplifies over the Arctic but misses the associated warm Arctic–cold continent pattern. The MSN EOF2 delineates a wavelike T2m pattern over the Pacific–North America region, which is rooted in the tropical forcing of the eastern Pacific-type El Niño–Southern Oscillation (ENSO). The MSN EOF3 shows a wavelike T2m pattern over the Pacific–North America region, which has an approximately 90° phase difference from that associated with MSN EOF2, and a loading center over midlatitude Eurasia. Its sources of predictability include the central Pacific-type ENSO and Eurasian snow cover. The MSN EOF4 reflects T2m variability surrounding the Tibetan Plateau, which is plausibly linked to the remote forcing of the Arctic sea ice. The information on the leading predictable patterns and their sources of predictability is further used to develop a calibration scheme to improve the prediction skill of T2m. The calibrated prediction skill in terms of the anomaly correlation coefficient improves significantly over midlatitude Eurasia in a leave-one-out cross-validation, implying a possible way to improve the wintertime T2m prediction in the SEAS5.
