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Abstract
Two dominant global-scale teleconnections—namely, western North Pacific–North American (WPNA) and circumglobal teleconnection (CGT)—in the Northern Hemisphere (NH) extratropics during boreal summer (June–August) have been identified as important sources for NH summer climate variability and predictability. An interdecadal shift in interannual variability and predictability of the WPNA and CGT that occurred around the late 1970s was investigated using reanalysis data and six coupled models’ retrospective forecast with a 1 May initial condition for the period 1960–79 (P1) and 1980–2005 (P2). The WPNA had a tight relationship with the decaying phase of El Niño–Southern Oscillation (ENSO) in P1, whereas it had a remarkably enhanced linkage with western North Pacific (WNP) summer monsoon rainfall in P2. The correlation coefficient between the WPNA and preceding ENSO (WNP monsoon rainfall) was reduced (increased) from −0.69 (0.1) in P1 to −0.60 (0.5) in P2. The CGT had a considerable connection with Indian summer monsoon rainfall (ISMR) in P1, whereas it had a strengthened relationship with the developing ENSO in P2. The correlation coefficient between the CGT and simultaneous ENSO (ISMR) was increased (decreased) from −0.41 (0.47) in P1 to −0.59 (0.24) in P2. Although dynamical models have difficulties in capturing the observed interdecadal changes, they are able to predict the interannual variation of the WPNA and CGT one month ahead, to some extent. The prediction skill of six models’ multimodel ensemble (MME) decreased (increased) from 0.78 (0.23) to 0.67 (0.67) for the WPNA (CGT) interannual variation. It is also noted that the spatial distribution of predictability and MME skill for 200-hPa geopotential height has been changed in relation to the changes in the WPNA and CGT.
Abstract
Two dominant global-scale teleconnections—namely, western North Pacific–North American (WPNA) and circumglobal teleconnection (CGT)—in the Northern Hemisphere (NH) extratropics during boreal summer (June–August) have been identified as important sources for NH summer climate variability and predictability. An interdecadal shift in interannual variability and predictability of the WPNA and CGT that occurred around the late 1970s was investigated using reanalysis data and six coupled models’ retrospective forecast with a 1 May initial condition for the period 1960–79 (P1) and 1980–2005 (P2). The WPNA had a tight relationship with the decaying phase of El Niño–Southern Oscillation (ENSO) in P1, whereas it had a remarkably enhanced linkage with western North Pacific (WNP) summer monsoon rainfall in P2. The correlation coefficient between the WPNA and preceding ENSO (WNP monsoon rainfall) was reduced (increased) from −0.69 (0.1) in P1 to −0.60 (0.5) in P2. The CGT had a considerable connection with Indian summer monsoon rainfall (ISMR) in P1, whereas it had a strengthened relationship with the developing ENSO in P2. The correlation coefficient between the CGT and simultaneous ENSO (ISMR) was increased (decreased) from −0.41 (0.47) in P1 to −0.59 (0.24) in P2. Although dynamical models have difficulties in capturing the observed interdecadal changes, they are able to predict the interannual variation of the WPNA and CGT one month ahead, to some extent. The prediction skill of six models’ multimodel ensemble (MME) decreased (increased) from 0.78 (0.23) to 0.67 (0.67) for the WPNA (CGT) interannual variation. It is also noted that the spatial distribution of predictability and MME skill for 200-hPa geopotential height has been changed in relation to the changes in the WPNA and CGT.
Abstract
In a tier-two seasonal prediction system, prior to AGCM integration, global SSTs should first be predicted as a boundary condition to the AGCM. In this study, a global SST prediction system has been developed as a part of the tier-two seasonal prediction system. This system uses predictions from four models—one dynamic, two statistical, and persistence—and a simple composite ensemble method is applied to these models. The simple composite ensemble prediction system has predictive skill over most of the global oceans for up to a 6-month forecast lead time. The simple ensemble method is also compared with other more sophisticated ensemble methods. The simple composite method has forecast skill comparable to the other ensemble methods over the ENSO region and significantly better skill outside the ENSO region.
Abstract
In a tier-two seasonal prediction system, prior to AGCM integration, global SSTs should first be predicted as a boundary condition to the AGCM. In this study, a global SST prediction system has been developed as a part of the tier-two seasonal prediction system. This system uses predictions from four models—one dynamic, two statistical, and persistence—and a simple composite ensemble method is applied to these models. The simple composite ensemble prediction system has predictive skill over most of the global oceans for up to a 6-month forecast lead time. The simple ensemble method is also compared with other more sophisticated ensemble methods. The simple composite method has forecast skill comparable to the other ensemble methods over the ENSO region and significantly better skill outside the ENSO region.
Abstract
Predictability of intraseasonal oscillation (ISO) relies on both initial conditions and lower boundary conditions (or atmosphere–ocean interaction). The atmospheric reanalysis datasets are commonly used as initial conditions. Here, the biases of three reanalysis datasets [the NCEP reanalysis 1 and 2 (NCEP-R1 and -R2) and the ECMWF Re-Analysis Interim (ERA-Interim)] in describing ISO were briefly revealed and the impacts of these biases as initial conditions on ISO prediction skills were assessed. A signal-recovery method is proposed to improve ISO prediction.
Although all three reanalyses underestimate the intensity of the equatorial eastward-propagating ISO, the overall quality of the ERA-Interim is better than the NCEP-R1 and -R2. When these reanalyses are used as initial conditions in the ECHAM4-University of Hawaii hybrid coupled model (UH-HCM), skillful ISO prediction reaches only about 1 week for both the 850-hPa zonal winds (U850) and rainfall over Southeast Asia and the global tropics. An enhanced nudging of the divergence field is shown to significantly improve the initial conditions, resulting in an extension of the skillful rainfall prediction by 2–4 days and U850 prediction by 5–10 days.
After recovering the ISO signals in the original reanalyses, the resultant initial conditions contain ISO strength closer to the observed, whereas the rainfall spatial pattern correlation in the ERA-Interim reanalysis drops. The resultant ISO prediction skills, however, are consistently extended for all the NCEP and ERA-Interim reanalyses. Using these signal-recovered reanalyses as initial conditions, the boreal summer ISO prediction skill measured with the Wheeler–Hendon index reaches 14 days. The U850 and rainfall prediction skills, respectively, reach 23 and 18 days over Southeast Asia. It is also found that small-scale synoptic weather disturbances in initial conditions generally increase ISO prediction skills. Both the UH-HCM and NCEP Climate Forecast System (CFS) suffer the prediction barrier over the Maritime Continent.
Abstract
Predictability of intraseasonal oscillation (ISO) relies on both initial conditions and lower boundary conditions (or atmosphere–ocean interaction). The atmospheric reanalysis datasets are commonly used as initial conditions. Here, the biases of three reanalysis datasets [the NCEP reanalysis 1 and 2 (NCEP-R1 and -R2) and the ECMWF Re-Analysis Interim (ERA-Interim)] in describing ISO were briefly revealed and the impacts of these biases as initial conditions on ISO prediction skills were assessed. A signal-recovery method is proposed to improve ISO prediction.
Although all three reanalyses underestimate the intensity of the equatorial eastward-propagating ISO, the overall quality of the ERA-Interim is better than the NCEP-R1 and -R2. When these reanalyses are used as initial conditions in the ECHAM4-University of Hawaii hybrid coupled model (UH-HCM), skillful ISO prediction reaches only about 1 week for both the 850-hPa zonal winds (U850) and rainfall over Southeast Asia and the global tropics. An enhanced nudging of the divergence field is shown to significantly improve the initial conditions, resulting in an extension of the skillful rainfall prediction by 2–4 days and U850 prediction by 5–10 days.
After recovering the ISO signals in the original reanalyses, the resultant initial conditions contain ISO strength closer to the observed, whereas the rainfall spatial pattern correlation in the ERA-Interim reanalysis drops. The resultant ISO prediction skills, however, are consistently extended for all the NCEP and ERA-Interim reanalyses. Using these signal-recovered reanalyses as initial conditions, the boreal summer ISO prediction skill measured with the Wheeler–Hendon index reaches 14 days. The U850 and rainfall prediction skills, respectively, reach 23 and 18 days over Southeast Asia. It is also found that small-scale synoptic weather disturbances in initial conditions generally increase ISO prediction skills. Both the UH-HCM and NCEP Climate Forecast System (CFS) suffer the prediction barrier over the Maritime Continent.
Abstract
Every dynamical climate prediction model has significant errors in its mean state and anomaly field, thus degrading its performance in climate prediction. In addition to correcting the model’s systematic errors in the mean state, it is also possible to correct systematic errors in the predicted anomalies by means of dynamical or statistical postprocessing. In this study, a new statistical model has been developed based on the pattern projection method in order to empirically correct the dynamical seasonal climate prediction. The strength of the present model lies in the objective and automatic selection of optimal predictor grid points. The statistical model was applied to systematic error correction of SST anomalies predicted by Seoul National University’s (SNU) coupled GCM and evaluated in terms of temporal correlation skill and standardized root-mean-square error. It turns out that the statistical error correction improves the SST prediction over most regions of the global ocean with most forecast lead times up to 6 months. In particular, the SST predictions over the western Pacific and Indian Ocean are improved significantly, where the SNU coupled GCM shows a large error.
Abstract
Every dynamical climate prediction model has significant errors in its mean state and anomaly field, thus degrading its performance in climate prediction. In addition to correcting the model’s systematic errors in the mean state, it is also possible to correct systematic errors in the predicted anomalies by means of dynamical or statistical postprocessing. In this study, a new statistical model has been developed based on the pattern projection method in order to empirically correct the dynamical seasonal climate prediction. The strength of the present model lies in the objective and automatic selection of optimal predictor grid points. The statistical model was applied to systematic error correction of SST anomalies predicted by Seoul National University’s (SNU) coupled GCM and evaluated in terms of temporal correlation skill and standardized root-mean-square error. It turns out that the statistical error correction improves the SST prediction over most regions of the global ocean with most forecast lead times up to 6 months. In particular, the SST predictions over the western Pacific and Indian Ocean are improved significantly, where the SNU coupled GCM shows a large error.
Abstract
Ensemble simulations of Asian–Australian monsoon (A–AM) anomalies were evaluated in 11 atmospheric general circulation models for the unprecedented El Niño period of September 1996–August 1998. The models' simulations of anomalous Asian summer rainfall patterns in the A–AM region (30°S–30°N, 40°–160°E) are considerably poorer than in the El Niño region. This is mainly due to a lack of skill over Southeast Asia and the western North Pacific (5°–30°N, 80°–150°E), which is a striking characteristic of all the models. The models' deficiencies result from failing to simulate correctly the relationship between the local summer rainfall and the SST anomalies over the Philippine Sea, the South China Sea, and the Bay of Bengal: the observed rainfall anomalies are negatively correlated with SST anomalies, whereas in nearly all models, the rainfall anomalies are positively correlated with SST anomalies. While the models' physical parameterizations have large uncertainties, this problem is primarily attributed to the experimental design in which the atmosphere is forced to respond passively to the specified SSTs, while in nature the SSTs result in part from the atmospheric forcing.
Regional monsoon dynamic indices are calculated for the Indian, the western North Pacific, and the Australian monsoons, respectively. Most models can realistically reproduce the western North Pacific and Australian monsoon, yet fail with the Indian monsoon. To see whether this is generally the case, a suite of five Seoul National University model runs with the same observed lower boundary forcing (and differing only in their initial conditions) was examined for the period 1950–98. The skill in the 49-yr ensemble simulations of the Indian monsoon is significantly higher than the skill for the period 1996–98. In other words for the unprecedented 1997/98 El Niño period, the models under study experience unusual difficulties in simulating the Indian monsoon circulation anomalies. Moreover, the observed Webster–Yang index shows a decreasing trend over the last 50 yr, a trend missed by the models' ensemble simulations.
Abstract
Ensemble simulations of Asian–Australian monsoon (A–AM) anomalies were evaluated in 11 atmospheric general circulation models for the unprecedented El Niño period of September 1996–August 1998. The models' simulations of anomalous Asian summer rainfall patterns in the A–AM region (30°S–30°N, 40°–160°E) are considerably poorer than in the El Niño region. This is mainly due to a lack of skill over Southeast Asia and the western North Pacific (5°–30°N, 80°–150°E), which is a striking characteristic of all the models. The models' deficiencies result from failing to simulate correctly the relationship between the local summer rainfall and the SST anomalies over the Philippine Sea, the South China Sea, and the Bay of Bengal: the observed rainfall anomalies are negatively correlated with SST anomalies, whereas in nearly all models, the rainfall anomalies are positively correlated with SST anomalies. While the models' physical parameterizations have large uncertainties, this problem is primarily attributed to the experimental design in which the atmosphere is forced to respond passively to the specified SSTs, while in nature the SSTs result in part from the atmospheric forcing.
Regional monsoon dynamic indices are calculated for the Indian, the western North Pacific, and the Australian monsoons, respectively. Most models can realistically reproduce the western North Pacific and Australian monsoon, yet fail with the Indian monsoon. To see whether this is generally the case, a suite of five Seoul National University model runs with the same observed lower boundary forcing (and differing only in their initial conditions) was examined for the period 1950–98. The skill in the 49-yr ensemble simulations of the Indian monsoon is significantly higher than the skill for the period 1996–98. In other words for the unprecedented 1997/98 El Niño period, the models under study experience unusual difficulties in simulating the Indian monsoon circulation anomalies. Moreover, the observed Webster–Yang index shows a decreasing trend over the last 50 yr, a trend missed by the models' ensemble simulations.
Abstract
Potential predictability of summer mean precipitation over the globe is investigated using data obtained from seasonal prediction experiments for 21 yr from 1979 to 1999 using the Korea Meteorological Administration–Seoul National University (KMA–SNU) seasonal prediction system. This experiment is a part of the Climate Variability and Predictability Program (CLIVAR) Seasonal Model Intercomparison Project II (SMIP II). The observed SSTs are used for the external boundary condition of the model integration; thus, the present study assesses the upper limit of predictability of the seasonal prediction system. The analysis shows that the tropical precipitation is largely controlled by the given SST condition and is thus predictable, particularly in the ENSO region. But the extratropical precipitation is less predictable due to the large contribution of the internal atmospheric processes to the seasonal mean. The systematic error of the ensemble mean prediction is particularly large in the subtropical western Pacific, where the air–sea interaction is active and thus the two-tier approach of the present prediction experiment is not appropriate for correct predictions in the region.
The statistical postprocessing method based on singular value decomposition corrects a large part of the systematic errors over the globe. In particular, about two-thirds of the total errors in the western Pacific are corrected by the postprocessing method. As a result, the potential predictability of the summer-mean precipitation is greatly enhanced over most of the globe by the statistical correction method; the 21-yr-averaged pattern-correlation value between the predictions and their observed counterparts is changed from 0.31 before the correction to 0.48 after the correction for the global domain and from 0.04 before the correction to 0.26 after the correction for the Asian monsoon and the western Pacific region.
Abstract
Potential predictability of summer mean precipitation over the globe is investigated using data obtained from seasonal prediction experiments for 21 yr from 1979 to 1999 using the Korea Meteorological Administration–Seoul National University (KMA–SNU) seasonal prediction system. This experiment is a part of the Climate Variability and Predictability Program (CLIVAR) Seasonal Model Intercomparison Project II (SMIP II). The observed SSTs are used for the external boundary condition of the model integration; thus, the present study assesses the upper limit of predictability of the seasonal prediction system. The analysis shows that the tropical precipitation is largely controlled by the given SST condition and is thus predictable, particularly in the ENSO region. But the extratropical precipitation is less predictable due to the large contribution of the internal atmospheric processes to the seasonal mean. The systematic error of the ensemble mean prediction is particularly large in the subtropical western Pacific, where the air–sea interaction is active and thus the two-tier approach of the present prediction experiment is not appropriate for correct predictions in the region.
The statistical postprocessing method based on singular value decomposition corrects a large part of the systematic errors over the globe. In particular, about two-thirds of the total errors in the western Pacific are corrected by the postprocessing method. As a result, the potential predictability of the summer-mean precipitation is greatly enhanced over most of the globe by the statistical correction method; the 21-yr-averaged pattern-correlation value between the predictions and their observed counterparts is changed from 0.31 before the correction to 0.48 after the correction for the global domain and from 0.04 before the correction to 0.26 after the correction for the Asian monsoon and the western Pacific region.
Abstract
The boreal summer intraseasonal oscillation (BSISO) is a dominant tropical mode with a period of 30–60 days, which offers an opportunity for intraseasonal forecasting of the Asian summer monsoon. The present study provides a preliminary, yet up-to-date, assessment of the prediction skill of the BSISO in four state-of-the-art models: the ECMWF model, the University of Hawaii (UH) model, the NCEP Climate Forecast System, version 2 (CFSv2), and version 1 for the 2008 summer (CFSv1), which is a common year of two international programs: the Year of Tropical Convection (YOTC) and Asian Monsoon Years (AMY). The mean prediction skill over the global tropics and Southeast Asia for first three models reaches about 1–2 (3) weeks for BSISO-related rainfall (850-hPa zonal wind), measured as the lead time when the spatial anomaly correlation coefficient drops to 0.5. The skill of CFSv1 is consistently lower than the other three. The strengths and weaknesses of the CFSv2, UH, and ECMWF models in forecasting the BSISO for this specific year are further revealed. The ECMWF and UH have relatively better performance for northward-propagating BSISO when the initial convection is near the equator, although they suffer from an early false BSISO onset when initial convection is in the off-equatorial monsoon trough. However, CFSv2 does not have a false onset problem when the initial convection is in monsoon trough, but it does have a problem with very slow northward propagation. After combining the forecasts of CFSv2 and UH into an equal-weighted multimodel ensemble, the resultant skill is slightly better than that of individual models. An empirical model shows a comparable skill with the dynamical models. A combined dynamical–empirical ensemble advances the intraseasonal forecast skill of BSISO-related rainfall to three weeks.
Abstract
The boreal summer intraseasonal oscillation (BSISO) is a dominant tropical mode with a period of 30–60 days, which offers an opportunity for intraseasonal forecasting of the Asian summer monsoon. The present study provides a preliminary, yet up-to-date, assessment of the prediction skill of the BSISO in four state-of-the-art models: the ECMWF model, the University of Hawaii (UH) model, the NCEP Climate Forecast System, version 2 (CFSv2), and version 1 for the 2008 summer (CFSv1), which is a common year of two international programs: the Year of Tropical Convection (YOTC) and Asian Monsoon Years (AMY). The mean prediction skill over the global tropics and Southeast Asia for first three models reaches about 1–2 (3) weeks for BSISO-related rainfall (850-hPa zonal wind), measured as the lead time when the spatial anomaly correlation coefficient drops to 0.5. The skill of CFSv1 is consistently lower than the other three. The strengths and weaknesses of the CFSv2, UH, and ECMWF models in forecasting the BSISO for this specific year are further revealed. The ECMWF and UH have relatively better performance for northward-propagating BSISO when the initial convection is near the equator, although they suffer from an early false BSISO onset when initial convection is in the off-equatorial monsoon trough. However, CFSv2 does not have a false onset problem when the initial convection is in monsoon trough, but it does have a problem with very slow northward propagation. After combining the forecasts of CFSv2 and UH into an equal-weighted multimodel ensemble, the resultant skill is slightly better than that of individual models. An empirical model shows a comparable skill with the dynamical models. A combined dynamical–empirical ensemble advances the intraseasonal forecast skill of BSISO-related rainfall to three weeks.
Abstract
This study assesses the changes in the tropical Pacific Ocean sea surface temperature (SST) trend and ENSO amplitude by comparing a historical run of the World Climate Research Programme Coupled Model Intercomparison Project (CMIP) phase-5 multimodel ensemble dataset (CMIP5) and the CMIP phase-3 dataset (CMIP3). The results indicate that the magnitude of the SST trend in the tropical Pacific basin has been significantly reduced from CMIP3 to CMIP5, which may be associated with the overestimation of the response to natural forcing and aerosols by including Earth system models in CMIP5. Moreover, the patterns of tropical warming over the second half of the twentieth century have changed from a La Niña–like structure in CMIP3 to an El Niño–like structure in CMIP5. Further analysis indicates that such changes in the background state of the tropical Pacific and an increase in the sensitivity of the atmospheric response to the SST changes in the eastern tropical Pacific have influenced the ENSO properties. In particular, the ratio of the SST anomaly variance in the eastern and western tropical Pacific increased from CMIP3 to CMIP5, indicating that a center of action associated with the ENSO amplitude has shifted to the east.
Abstract
This study assesses the changes in the tropical Pacific Ocean sea surface temperature (SST) trend and ENSO amplitude by comparing a historical run of the World Climate Research Programme Coupled Model Intercomparison Project (CMIP) phase-5 multimodel ensemble dataset (CMIP5) and the CMIP phase-3 dataset (CMIP3). The results indicate that the magnitude of the SST trend in the tropical Pacific basin has been significantly reduced from CMIP3 to CMIP5, which may be associated with the overestimation of the response to natural forcing and aerosols by including Earth system models in CMIP5. Moreover, the patterns of tropical warming over the second half of the twentieth century have changed from a La Niña–like structure in CMIP3 to an El Niño–like structure in CMIP5. Further analysis indicates that such changes in the background state of the tropical Pacific and an increase in the sensitivity of the atmospheric response to the SST changes in the eastern tropical Pacific have influenced the ENSO properties. In particular, the ratio of the SST anomaly variance in the eastern and western tropical Pacific increased from CMIP3 to CMIP5, indicating that a center of action associated with the ENSO amplitude has shifted to the east.
Abstract
Multimodel ensemble (MME) seasonal forecasts are analyzed to evaluate numerical model performance in predicting the leading forced atmospheric circulation pattern over the extratropical Northern Hemisphere (NH). Results show that the time evolution of the leading tropical Pacific sea surface temperature (SST)-coupled atmospheric pattern (MCA1), which is obtained by applying a maximum covariance analysis (MCA) between 500-hPa geopotential height (Z 500) in the extratropical NH and SST in the tropical Pacific Ocean, can be predicted with a significant skill in March–May (MAM), June–August (JJA), and December–February (DJF) one month ahead. However, most models perform poorly in capturing the time variation of MCA1 in September–November (SON) with 1 August initial condition. Two possible reasons for the models’ low skill in SON are identified. First, the models have the most pronounced errors in the mean state of SST and precipitation along the central equatorial Pacific. Because of the link between the divergent circulation forced by tropical heating and the midlatitude atmospheric circulation, errors in the mean state of tropical SST and precipitation may lead to a degradation of midlatitude forecast skill. Second, examination of the potential predictability of the atmosphere, estimated by the ratio of the total variance to the variance of the model forecasts due to internal dynamics, shows that the atmospheric potential predictability over the North Pacific–North American (NPNA) region is the lowest in SON compared to the other three seasons. The low ratio in SON is due to a low variance associated with external forcing and a high variance related to atmospheric internal processes over this area.
Abstract
Multimodel ensemble (MME) seasonal forecasts are analyzed to evaluate numerical model performance in predicting the leading forced atmospheric circulation pattern over the extratropical Northern Hemisphere (NH). Results show that the time evolution of the leading tropical Pacific sea surface temperature (SST)-coupled atmospheric pattern (MCA1), which is obtained by applying a maximum covariance analysis (MCA) between 500-hPa geopotential height (Z 500) in the extratropical NH and SST in the tropical Pacific Ocean, can be predicted with a significant skill in March–May (MAM), June–August (JJA), and December–February (DJF) one month ahead. However, most models perform poorly in capturing the time variation of MCA1 in September–November (SON) with 1 August initial condition. Two possible reasons for the models’ low skill in SON are identified. First, the models have the most pronounced errors in the mean state of SST and precipitation along the central equatorial Pacific. Because of the link between the divergent circulation forced by tropical heating and the midlatitude atmospheric circulation, errors in the mean state of tropical SST and precipitation may lead to a degradation of midlatitude forecast skill. Second, examination of the potential predictability of the atmosphere, estimated by the ratio of the total variance to the variance of the model forecasts due to internal dynamics, shows that the atmospheric potential predictability over the North Pacific–North American (NPNA) region is the lowest in SON compared to the other three seasons. The low ratio in SON is due to a low variance associated with external forcing and a high variance related to atmospheric internal processes over this area.
Abstract
Understanding the change of equatorial Pacific trade winds is pivotal for understanding the global mean temperature change and the El Niño–Southern Oscillation (ENSO) property change. The weakening of the Walker circulation due to anthropogenic greenhouse gas (GHG) forcing was suggested as one of the most robust phenomena in current climate models by examining zonal sea level pressure gradient over the tropical Pacific. This study explores another component of the Walker circulation change focusing on equatorial Pacific trade wind change. Model sensitivity experiments demonstrate that the direct/fast response due to GHG forcing is to increase the trade winds, especially over the equatorial central-western Pacific (ECWP) (5°S–5°N, 140°E–150°W), while the indirect/slow response associated with sea surface temperature (SST) warming weakens the trade winds.
Further, analysis of the results from 19 models in phase 5 of the Coupled Model Intercomparison Project (CMIP5) and the Parallel Ocean Program (POP)–Ocean Atmosphere Sea Ice Soil (OASIS)–ECHAM model (POEM) shows that the projected weakening of the trades is robust only in the equatorial eastern Pacific (EEP) ( 5°S–5°N, 150°–80°W), but highly uncertain over the ECWP with 9 out of 19 CMIP5 models producing intensified trades. The prominent and robust weakening of EEP trades is suggested to be mainly driven by a top-down mechanism: the mean vertical advection of more upper-tropospheric warming downward to generate a cyclonic circulation anomaly in the southeast tropical Pacific. In the ECWP, the large intermodel spread is primarily linked to model diversity in simulating the relative warming of the equatorial Pacific versus the tropical mean sea surface temperature. The possible root causes of the uncertainty for the trade wind change are also discussed.
Abstract
Understanding the change of equatorial Pacific trade winds is pivotal for understanding the global mean temperature change and the El Niño–Southern Oscillation (ENSO) property change. The weakening of the Walker circulation due to anthropogenic greenhouse gas (GHG) forcing was suggested as one of the most robust phenomena in current climate models by examining zonal sea level pressure gradient over the tropical Pacific. This study explores another component of the Walker circulation change focusing on equatorial Pacific trade wind change. Model sensitivity experiments demonstrate that the direct/fast response due to GHG forcing is to increase the trade winds, especially over the equatorial central-western Pacific (ECWP) (5°S–5°N, 140°E–150°W), while the indirect/slow response associated with sea surface temperature (SST) warming weakens the trade winds.
Further, analysis of the results from 19 models in phase 5 of the Coupled Model Intercomparison Project (CMIP5) and the Parallel Ocean Program (POP)–Ocean Atmosphere Sea Ice Soil (OASIS)–ECHAM model (POEM) shows that the projected weakening of the trades is robust only in the equatorial eastern Pacific (EEP) ( 5°S–5°N, 150°–80°W), but highly uncertain over the ECWP with 9 out of 19 CMIP5 models producing intensified trades. The prominent and robust weakening of EEP trades is suggested to be mainly driven by a top-down mechanism: the mean vertical advection of more upper-tropospheric warming downward to generate a cyclonic circulation anomaly in the southeast tropical Pacific. In the ECWP, the large intermodel spread is primarily linked to model diversity in simulating the relative warming of the equatorial Pacific versus the tropical mean sea surface temperature. The possible root causes of the uncertainty for the trade wind change are also discussed.
