Search Results
You are looking at 1 - 10 of 17 items for
- Author or Editor: Ke Fan x
- Refine by Access: All Content x
Abstract
This paper presents a year-by-year incremental approach to forecasting the Atlantic named storm frequency (ATSF) for the hurricane season (1 June–30 November). The year-by-year increase or decrease in the ATSF is first forecasted to yield a net ATSF prediction. Six key predictors for the year-by-year increment in the number of Atlantic named tropical storms have been identified that are available before 1 May. The forecast model for the year-by-year increment of the ATSF is first established using a multilinear regression method based on data taken from the years 1965–99, and the forecast model of the ATSF is then derived. The prediction model for the ATSF shows good prediction skill. Compared to the climatological average mean absolute error (MAE) of 4.1, the percentage improvement in the MAE is 29% for the hindcast period of 2004–09 and 46% for the cross-validation test from 1985 to 2009 (26 yr). This work demonstrates that the year-by-year incremental approach has the potential to improve the operational forecasting skill for the ATSF.
Abstract
This paper presents a year-by-year incremental approach to forecasting the Atlantic named storm frequency (ATSF) for the hurricane season (1 June–30 November). The year-by-year increase or decrease in the ATSF is first forecasted to yield a net ATSF prediction. Six key predictors for the year-by-year increment in the number of Atlantic named tropical storms have been identified that are available before 1 May. The forecast model for the year-by-year increment of the ATSF is first established using a multilinear regression method based on data taken from the years 1965–99, and the forecast model of the ATSF is then derived. The prediction model for the ATSF shows good prediction skill. Compared to the climatological average mean absolute error (MAE) of 4.1, the percentage improvement in the MAE is 29% for the hindcast period of 2004–09 and 46% for the cross-validation test from 1985 to 2009 (26 yr). This work demonstrates that the year-by-year incremental approach has the potential to improve the operational forecasting skill for the ATSF.
Abstract
This paper presents a new approach for forecasting the typhoon frequency of the western North Pacific (WNP). The year-to-year increase or decrease in typhoon frequency is first forecasted to yield a net typhoon frequency prediction. Five key predictors for the year-to-year increment in the number of typhoons in the WNP have been identified, and a forecast model is established using a multilinear regression method based on data taken from 1965 to 2001. Using the forecast model, a hindcast of the typhoon frequency of the WNP during 2002–07 is made. The model exhibited a reasonably close fit for the period 1965–2007, including the larger anomalies in 1997 and 1998. It also accounted for the smaller variability of the typhoon frequency of the WNP during the validation period 2002–07 with an average root-mean-square error (RMSE) of 1.3 (2.85) during 2002–07 (1965–2001). The cross-validation test of the prediction model shows that the new approach and the prediction model demonstrate better prediction skill when compared to the models established based on typhoon frequency rather than the typhoon frequency increment. Thus, this new approach has the potential to improve the operational forecasting skill for typhoon frequency in the WNP.
Abstract
This paper presents a new approach for forecasting the typhoon frequency of the western North Pacific (WNP). The year-to-year increase or decrease in typhoon frequency is first forecasted to yield a net typhoon frequency prediction. Five key predictors for the year-to-year increment in the number of typhoons in the WNP have been identified, and a forecast model is established using a multilinear regression method based on data taken from 1965 to 2001. Using the forecast model, a hindcast of the typhoon frequency of the WNP during 2002–07 is made. The model exhibited a reasonably close fit for the period 1965–2007, including the larger anomalies in 1997 and 1998. It also accounted for the smaller variability of the typhoon frequency of the WNP during the validation period 2002–07 with an average root-mean-square error (RMSE) of 1.3 (2.85) during 2002–07 (1965–2001). The cross-validation test of the prediction model shows that the new approach and the prediction model demonstrate better prediction skill when compared to the models established based on typhoon frequency rather than the typhoon frequency increment. Thus, this new approach has the potential to improve the operational forecasting skill for typhoon frequency in the WNP.
Abstract
A new statistical forecast scheme, referred to as scheme 1, is developed using observed autumn Atlantic sea surface temperature (SST) and Eurasian snow cover in the preceding autumn to predict the upcoming winter North Atlantic Oscillation (NAO) using the year-to-year increment prediction approach (i.e., DY approach). Two predictors for the year-to-year increment are identified that are available in the preceding autumn. Cross-validation tests for the period 1950–2011 and independent hindcasts for the period 1990–2011 are performed to validate the prediction ability of the proposed technique. The cross-validation test results for 1950–2011 reveal a high correlation coefficient of 0.52 (0.58) between the predicted and observed NAO indices (DY of the NAO). The model also successfully predicts the independent hindcasts for the period 1990–2011 with a correlation coefficient of 0.55 (0.74). In addition, scheme 0 (i.e., anomaly approach) is established using the SST and snow cover anomalies during the preceding autumn. Compared with scheme 0, this new prediction model has higher predictive skill in reproducing the interdecadal variability of NAO. Therefore, this study provides an effective climate prediction scheme for the interannual and interdecadal variability of NAO in boreal winter.
Abstract
A new statistical forecast scheme, referred to as scheme 1, is developed using observed autumn Atlantic sea surface temperature (SST) and Eurasian snow cover in the preceding autumn to predict the upcoming winter North Atlantic Oscillation (NAO) using the year-to-year increment prediction approach (i.e., DY approach). Two predictors for the year-to-year increment are identified that are available in the preceding autumn. Cross-validation tests for the period 1950–2011 and independent hindcasts for the period 1990–2011 are performed to validate the prediction ability of the proposed technique. The cross-validation test results for 1950–2011 reveal a high correlation coefficient of 0.52 (0.58) between the predicted and observed NAO indices (DY of the NAO). The model also successfully predicts the independent hindcasts for the period 1990–2011 with a correlation coefficient of 0.55 (0.74). In addition, scheme 0 (i.e., anomaly approach) is established using the SST and snow cover anomalies during the preceding autumn. Compared with scheme 0, this new prediction model has higher predictive skill in reproducing the interdecadal variability of NAO. Therefore, this study provides an effective climate prediction scheme for the interannual and interdecadal variability of NAO in boreal winter.
Abstract
A new scheme is developed to improve the seasonal prediction of summer precipitation in the East Asian and western Pacific region. The scheme is applied to the Development of a European Multimodel Ensemble System for Seasonal to Interannual Prediction (DEMETER) results. The new scheme is designed to consider both model predictions and observed spatial patterns of historical “analog years.” In this paper, the anomaly pattern correlation coefficient (ACC) between the prediction and the observation, as well as the root-mean-square error, is used to measure the prediction skill. For the prediction of summer precipitation in East Asia and the western Pacific (0°–40°N, 80°–130°E), the prediction skill for the six model ensemble hindcasts for the years of 1979–2001 was increased to 0.22 by using the new scheme from 0.12 for the original scheme. All models were initiated in May and were composed of nine member predictions, and all showed improvement when applying the new scheme. The skill levels of the predictions for the six models increased from 0.08, 0.08, 0.01, 0.14, −0.07, and 0.07 for the original scheme to 0.11, 0.14, 0.10, 0.22, 0.04, and 0.13, respectively, for the new scheme.
Abstract
A new scheme is developed to improve the seasonal prediction of summer precipitation in the East Asian and western Pacific region. The scheme is applied to the Development of a European Multimodel Ensemble System for Seasonal to Interannual Prediction (DEMETER) results. The new scheme is designed to consider both model predictions and observed spatial patterns of historical “analog years.” In this paper, the anomaly pattern correlation coefficient (ACC) between the prediction and the observation, as well as the root-mean-square error, is used to measure the prediction skill. For the prediction of summer precipitation in East Asia and the western Pacific (0°–40°N, 80°–130°E), the prediction skill for the six model ensemble hindcasts for the years of 1979–2001 was increased to 0.22 by using the new scheme from 0.12 for the original scheme. All models were initiated in May and were composed of nine member predictions, and all showed improvement when applying the new scheme. The skill levels of the predictions for the six models increased from 0.08, 0.08, 0.01, 0.14, −0.07, and 0.07 for the original scheme to 0.11, 0.14, 0.10, 0.22, 0.04, and 0.13, respectively, for the new scheme.
Abstract
The subseasonal variability of winter air temperature in China during 2021/22 underwent significant changes, showing warm, warm, and cold anomalies during 2–23 December 2021 (P1), 1–27 January 2022 (P2), and 28 January–24 February 2022 (P3). The strong (weak) zonal circulation over East Asia led to positive (negative) surface air temperature anomalies (SATAs) during P1 and P2 (P3). The position of the Siberian high affected the distribution of the warmest center of SATA over northeastern and northwestern China in P1 and P2, respectively. Further investigations indicated that intraseasonal components (10–90 days) primarily drove the warm-to-cold transition in China during P2 and P3, contributing to 79.5% of the variance in SATA in winter 2021/22. Strong (weak) East Asian intraseasonal zonal circulations and positive (negative) meridional wind anomalies over China–Lake Baikal led to warm (cold) anomalies over China during P2 (P3). East Asian circulation alternations from P2 to P3 were associated with a shift in intraseasonal geopotential height anomalies over the North Atlantic region from positive to negative in the mid- to high troposphere through the propagation of north and south branch wave trains. The reversal of the North Atlantic geopotential height anomalies between P2 and P3 was modulated by intraseasonal higher-latitude SST anomalies over the North Atlantic and the location of intraseasonal stratospheric polar vortex. Furthermore, the intensified south branch wave train from the Indian Peninsula to China in the mid- to high troposphere was associated with active convection over the tropical western Indian Ocean during P3. These processes could be verified by using the linear baroclinic model.
Abstract
The subseasonal variability of winter air temperature in China during 2021/22 underwent significant changes, showing warm, warm, and cold anomalies during 2–23 December 2021 (P1), 1–27 January 2022 (P2), and 28 January–24 February 2022 (P3). The strong (weak) zonal circulation over East Asia led to positive (negative) surface air temperature anomalies (SATAs) during P1 and P2 (P3). The position of the Siberian high affected the distribution of the warmest center of SATA over northeastern and northwestern China in P1 and P2, respectively. Further investigations indicated that intraseasonal components (10–90 days) primarily drove the warm-to-cold transition in China during P2 and P3, contributing to 79.5% of the variance in SATA in winter 2021/22. Strong (weak) East Asian intraseasonal zonal circulations and positive (negative) meridional wind anomalies over China–Lake Baikal led to warm (cold) anomalies over China during P2 (P3). East Asian circulation alternations from P2 to P3 were associated with a shift in intraseasonal geopotential height anomalies over the North Atlantic region from positive to negative in the mid- to high troposphere through the propagation of north and south branch wave trains. The reversal of the North Atlantic geopotential height anomalies between P2 and P3 was modulated by intraseasonal higher-latitude SST anomalies over the North Atlantic and the location of intraseasonal stratospheric polar vortex. Furthermore, the intensified south branch wave train from the Indian Peninsula to China in the mid- to high troposphere was associated with active convection over the tropical western Indian Ocean during P3. These processes could be verified by using the linear baroclinic model.
Abstract
How the Atlantic multidecadal oscillation (AMO) affects El Niño–related signals in Southeast Asia is investigated in this study on a subseasonal scale. Based on observational and reanalysis data, as well as numerical model simulations, El Niño–related precipitation anomalies are analyzed for AMO positive and negative phases, which reveals a time-dependent modulation of the AMO. 1) In May–June, the AMO influences the precipitation in southern China (SC) and the Indochina peninsula (ICP) by modulating the El Niño–related air–sea interaction over the western North Pacific (WNP). During negative AMO phases, cold sea surface temperature anomalies (SSTAs) over the WNP favor the maintaining of the WNP anomalous anticyclone (WNPAC). The associated southerly (westerly) anomalies on the northwest (southwest) flank of the WNPAC enhance (reduce) the climatological moisture transport to SC (the ICP) and result in wetter (drier) than normal conditions. In contrast, during positive AMO phases, weak SSTAs over the WNP lead to limited influence of El Niño on precipitation in Southeast Asia. 2) In July–August, the teleconnection impact from the North Atlantic is more manifest than that in May–June. During positive AMO phases, the warmer than normal North Atlantic favors anomalous wave trains, which propagate along the “great circle route” and result in positive pressure anomalies over SC, consequently suppressing precipitation in SC and the ICP. During negative AMO phases, the anomalous wave trains tend to propagate eastward from Europe to Northeast Asia along the summer Asian jet, exerting limited influence on Southeast Asia.
Abstract
How the Atlantic multidecadal oscillation (AMO) affects El Niño–related signals in Southeast Asia is investigated in this study on a subseasonal scale. Based on observational and reanalysis data, as well as numerical model simulations, El Niño–related precipitation anomalies are analyzed for AMO positive and negative phases, which reveals a time-dependent modulation of the AMO. 1) In May–June, the AMO influences the precipitation in southern China (SC) and the Indochina peninsula (ICP) by modulating the El Niño–related air–sea interaction over the western North Pacific (WNP). During negative AMO phases, cold sea surface temperature anomalies (SSTAs) over the WNP favor the maintaining of the WNP anomalous anticyclone (WNPAC). The associated southerly (westerly) anomalies on the northwest (southwest) flank of the WNPAC enhance (reduce) the climatological moisture transport to SC (the ICP) and result in wetter (drier) than normal conditions. In contrast, during positive AMO phases, weak SSTAs over the WNP lead to limited influence of El Niño on precipitation in Southeast Asia. 2) In July–August, the teleconnection impact from the North Atlantic is more manifest than that in May–June. During positive AMO phases, the warmer than normal North Atlantic favors anomalous wave trains, which propagate along the “great circle route” and result in positive pressure anomalies over SC, consequently suppressing precipitation in SC and the ICP. During negative AMO phases, the anomalous wave trains tend to propagate eastward from Europe to Northeast Asia along the summer Asian jet, exerting limited influence on Southeast Asia.
Abstract
The interaction between El Niño–Southern Oscillation (ENSO) and the South China Sea summer monsoon (SCSSM) modulated by the Atlantic multidecadal oscillation (AMO) is investigated in this study. On one hand, the influence of the decaying phase of ENSO on the SCSSM is stronger during negative phases of the AMO than during positive phases. During negative phases of the AMO, El Niño (La Niña) with relatively larger variability leads to a western North Pacific anomalous anticyclone (cyclone) that persists from the ENSO mature winter to the ENSO decaying summer, weakening (strengthening) the SCSSM; on the contrary, during positive phases of the AMO, ENSO with relatively weaker variability cannot influence the SCSSM significantly. On the other hand, the SCSSM has a closer relationship with the subsequent ENSO development during positive phases of the AMO than during negative phases. During positive phases of the AMO, atmospheric teleconnections induced by the warmer North Atlantic result in low pressure and cyclonic anomalies over the South China Sea. Consequently, the stronger than normal SCSSM is accompanied by significant westerly wind anomalies over the western tropical Pacific, which favor the development of El Niño events. However, during negative phases of the AMO, the SCSSM-related westerly wind anomalies are rather weak, having a nonsignificant influence on El Niño development. The results are also demonstrated in model simulations from phase 5 of the Coupled Model Intercomparison Project (CMIP5).
Abstract
The interaction between El Niño–Southern Oscillation (ENSO) and the South China Sea summer monsoon (SCSSM) modulated by the Atlantic multidecadal oscillation (AMO) is investigated in this study. On one hand, the influence of the decaying phase of ENSO on the SCSSM is stronger during negative phases of the AMO than during positive phases. During negative phases of the AMO, El Niño (La Niña) with relatively larger variability leads to a western North Pacific anomalous anticyclone (cyclone) that persists from the ENSO mature winter to the ENSO decaying summer, weakening (strengthening) the SCSSM; on the contrary, during positive phases of the AMO, ENSO with relatively weaker variability cannot influence the SCSSM significantly. On the other hand, the SCSSM has a closer relationship with the subsequent ENSO development during positive phases of the AMO than during negative phases. During positive phases of the AMO, atmospheric teleconnections induced by the warmer North Atlantic result in low pressure and cyclonic anomalies over the South China Sea. Consequently, the stronger than normal SCSSM is accompanied by significant westerly wind anomalies over the western tropical Pacific, which favor the development of El Niño events. However, during negative phases of the AMO, the SCSSM-related westerly wind anomalies are rather weak, having a nonsignificant influence on El Niño development. The results are also demonstrated in model simulations from phase 5 of the Coupled Model Intercomparison Project (CMIP5).
Abstract
The features and causes of the leading intermonth modes of winter surface air temperature anomalies (SATA) over China are investigated, and associated prediction models are developed. The first three intermonth modes of winter SATA over China are obtained by extended empirical orthogonal function (i.e., EEOF1–3) analysis. The results show that EEOF1 represents consistent variations in the whole winter, with a variance contribution of 32.3%, whereas EEOF2 and EEOF3 show spatiotemporally inconsistent changes, and their variance contributions are 16.9% and 12.5%, respectively. EEOF2 has out-of-phase variations between December and January–February, and EEOF3 exhibits a temporal warm–cold alternating pattern, with spatially reversing changes over northwestern and southern China. However, the Climate Forecast System, version 2 (CFSv2) presents a limited prediction skill for winter SATA over China and their intermonth modes. Further investigations indicate that the September sea ice over the Barents–Laptev Seas, the November snow cover over western Europe and East Asia, and the November northern Atlantic sea surface temperature can be, respectively, adopted to develop prediction schemes for the consistent mode (EEOF1 scheme) and two inconsistent modes (EEOF2 and EEOF3 schemes) based on specific mechanisms. These schemes show effective performances in predicting both individual modes and the reconstruction field of SATA over China. The temporal correlation coefficients (TCCs) between cross-validation results and observations are 0.48, 0.51, and 0.31 for the EEOF1–3 modes, respectively (the 90% confidence level is 0.27). For the reconstruction field, the TCCs are 0.40, 0.27, and 0.45 in December, January, and February, respectively, which are much higher than those of the CFSv2 outputs (0.23, −0.16, and −0.09).
Abstract
The features and causes of the leading intermonth modes of winter surface air temperature anomalies (SATA) over China are investigated, and associated prediction models are developed. The first three intermonth modes of winter SATA over China are obtained by extended empirical orthogonal function (i.e., EEOF1–3) analysis. The results show that EEOF1 represents consistent variations in the whole winter, with a variance contribution of 32.3%, whereas EEOF2 and EEOF3 show spatiotemporally inconsistent changes, and their variance contributions are 16.9% and 12.5%, respectively. EEOF2 has out-of-phase variations between December and January–February, and EEOF3 exhibits a temporal warm–cold alternating pattern, with spatially reversing changes over northwestern and southern China. However, the Climate Forecast System, version 2 (CFSv2) presents a limited prediction skill for winter SATA over China and their intermonth modes. Further investigations indicate that the September sea ice over the Barents–Laptev Seas, the November snow cover over western Europe and East Asia, and the November northern Atlantic sea surface temperature can be, respectively, adopted to develop prediction schemes for the consistent mode (EEOF1 scheme) and two inconsistent modes (EEOF2 and EEOF3 schemes) based on specific mechanisms. These schemes show effective performances in predicting both individual modes and the reconstruction field of SATA over China. The temporal correlation coefficients (TCCs) between cross-validation results and observations are 0.48, 0.51, and 0.31 for the EEOF1–3 modes, respectively (the 90% confidence level is 0.27). For the reconstruction field, the TCCs are 0.40, 0.27, and 0.45 in December, January, and February, respectively, which are much higher than those of the CFSv2 outputs (0.23, −0.16, and −0.09).
Abstract
This study focuses on the month-to-month variability of winter temperature anomalies over Northeast China (NECTA), especially the out-of-phase change between December and January–February (colder than normal in December and warmer than normal in January–February, and vice versa), which accounts for 30% of the past 37 years (1980–2016). Our analysis shows that the variability of sea ice concentration (SIC) in the preceding November over the Davis Strait–Baffin Bay (SIC_DSBB) mainly affects NECTA in December, whereas the SIC over the Barents–Kara Sea (SIC_BKS) significantly impacts NECTA in January–February. A possible reason for the different effects of SIC_DSBB and SIC_BKS on NECTA is that the month-to-month increments (here called DM) of SIC over these two areas between October and November are different. A smaller DM of SIC_DSBB in November can generate eastward-propagating Rossby waves toward East Asia, whereas a larger DM of SIC_BKS can affect upward-propagating stationary Rossby waves toward the stratosphere in November. Less than normal SIC_DSBB in November corresponds to a negative phase of the sea surface temperature tripole pattern over the North Atlantic, which contributes to a negative phase of the North Atlantic Oscillation (NAO)-like geopotential height anomalies via the eddy-feedback mechanism, ultimately favoring cold conditions over Northeast China. However, positive November SIC_BKS anomalies can suppress upward-propagating Rossby waves that originate from the troposphere in November, strengthening the stratospheric polar vortex and leading to a positive phase of an Arctic Oscillation (AO)-like pattern in the stratosphere. Subsequently, these stratospheric anomalies propagate downward, causing the AO-like pattern in the troposphere in January–February, favoring warm conditions in Northeast China, and vice versa.
Abstract
This study focuses on the month-to-month variability of winter temperature anomalies over Northeast China (NECTA), especially the out-of-phase change between December and January–February (colder than normal in December and warmer than normal in January–February, and vice versa), which accounts for 30% of the past 37 years (1980–2016). Our analysis shows that the variability of sea ice concentration (SIC) in the preceding November over the Davis Strait–Baffin Bay (SIC_DSBB) mainly affects NECTA in December, whereas the SIC over the Barents–Kara Sea (SIC_BKS) significantly impacts NECTA in January–February. A possible reason for the different effects of SIC_DSBB and SIC_BKS on NECTA is that the month-to-month increments (here called DM) of SIC over these two areas between October and November are different. A smaller DM of SIC_DSBB in November can generate eastward-propagating Rossby waves toward East Asia, whereas a larger DM of SIC_BKS can affect upward-propagating stationary Rossby waves toward the stratosphere in November. Less than normal SIC_DSBB in November corresponds to a negative phase of the sea surface temperature tripole pattern over the North Atlantic, which contributes to a negative phase of the North Atlantic Oscillation (NAO)-like geopotential height anomalies via the eddy-feedback mechanism, ultimately favoring cold conditions over Northeast China. However, positive November SIC_BKS anomalies can suppress upward-propagating Rossby waves that originate from the troposphere in November, strengthening the stratospheric polar vortex and leading to a positive phase of an Arctic Oscillation (AO)-like pattern in the stratosphere. Subsequently, these stratospheric anomalies propagate downward, causing the AO-like pattern in the troposphere in January–February, favoring warm conditions in Northeast China, and vice versa.
Abstract
The summer Asian–Pacific oscillation (APO) is a dominant teleconnection pattern over the extratropical Northern Hemisphere that links the large-scale atmospheric circulation anomalies over the Asian–North Pacific Ocean sector. In this study, the direct Development of a European Multimodel Ensemble System for Seasonal-to-Interannual Prediction (DEMETER) model outputs from 1960 to 2001, which are limited in predicting the interannual variability of the summer Asian upper-tropospheric temperature and the decadal variations, are applied using the interannual increment approach to improve the predictions of the summer APO. By treating the year-to-year increment as the predictand, the interannual increment scheme is shown to significantly improve the predictive ability for the interannual variability of the summer Asian upper-tropospheric temperature and the decadal variations. The improvements for the interannual and interdecadal summer APO variability predictions in the interannual increment scheme relative to the original scheme are clear and significant. Compared with the DEMETER direct outputs, the statistical model with two predictors of APO and sea surface temperature anomaly over the Atlantic shows a significantly improved ability to predict the interannual variability of the summer rainfall over the middle and lower reaches of the Yangtze River valley (SRYR). This study therefore describes a more efficient approach for predicting the APO and the SRYR.
Abstract
The summer Asian–Pacific oscillation (APO) is a dominant teleconnection pattern over the extratropical Northern Hemisphere that links the large-scale atmospheric circulation anomalies over the Asian–North Pacific Ocean sector. In this study, the direct Development of a European Multimodel Ensemble System for Seasonal-to-Interannual Prediction (DEMETER) model outputs from 1960 to 2001, which are limited in predicting the interannual variability of the summer Asian upper-tropospheric temperature and the decadal variations, are applied using the interannual increment approach to improve the predictions of the summer APO. By treating the year-to-year increment as the predictand, the interannual increment scheme is shown to significantly improve the predictive ability for the interannual variability of the summer Asian upper-tropospheric temperature and the decadal variations. The improvements for the interannual and interdecadal summer APO variability predictions in the interannual increment scheme relative to the original scheme are clear and significant. Compared with the DEMETER direct outputs, the statistical model with two predictors of APO and sea surface temperature anomaly over the Atlantic shows a significantly improved ability to predict the interannual variability of the summer rainfall over the middle and lower reaches of the Yangtze River valley (SRYR). This study therefore describes a more efficient approach for predicting the APO and the SRYR.
