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- Author or Editor: Qigang Wu x
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Abstract
A lagged maximum covariance analysis (MCA) is utilized to investigate large-scale patterns of covariability between sea surface temperature (SST) in the global tropics and 500-mb geopotential height (Z500) in the extratropics at monthly to interannual time scales distinct from the conventional El Niño–Southern Oscillation (ENSO) signal during the Northern Hemisphere (NH) winter. The first MCA mode indicates a strong impact of tropical SST anomalies associated with ENSO on the extratropical atmosphere. The second MCA mode corresponds with coupling between Arctic Oscillation (AO)-like atmospheric variations and tropical SST anomalies. An AO-like MCA mode appears to depict an atmosphere-to-ocean forcing, in which the tropical ocean responds to the higher extratropical AO-like atmospheric anomalies with an intraseasonal time lag. In winter, AO-like atmospheric variability is associated with the northern tropical Atlantic mode and the tropical Pacific ENSO Modoki mode through enhanced or weakened trade winds.
The above forced SST anomalies by the AO-like variability may play a role in the subsequent evolution of the conventional ENSO phenomena.
Abstract
A lagged maximum covariance analysis (MCA) is utilized to investigate large-scale patterns of covariability between sea surface temperature (SST) in the global tropics and 500-mb geopotential height (Z500) in the extratropics at monthly to interannual time scales distinct from the conventional El Niño–Southern Oscillation (ENSO) signal during the Northern Hemisphere (NH) winter. The first MCA mode indicates a strong impact of tropical SST anomalies associated with ENSO on the extratropical atmosphere. The second MCA mode corresponds with coupling between Arctic Oscillation (AO)-like atmospheric variations and tropical SST anomalies. An AO-like MCA mode appears to depict an atmosphere-to-ocean forcing, in which the tropical ocean responds to the higher extratropical AO-like atmospheric anomalies with an intraseasonal time lag. In winter, AO-like atmospheric variability is associated with the northern tropical Atlantic mode and the tropical Pacific ENSO Modoki mode through enhanced or weakened trade winds.
The above forced SST anomalies by the AO-like variability may play a role in the subsequent evolution of the conventional ENSO phenomena.
Abstract
A lagged maximum covariance analysis (MCA) is applied to investigate the linear covariability between monthly sea ice concentration (SIC) and 500-mb geopotential height (Z500) in the Southern Hemisphere (SH). The dominant signal is the atmospheric forcing of SIC anomalies throughout the year, but statistically significant covariances are also found between austral springtime Z500 and prior SIC anomalies up to four months earlier. The MCA pattern is characterized by an Antarctic dipole (ADP)-like pattern in SIC and a positively polarized Antarctic Oscillation (AAO) in Z500. Such long lead-time covariance suggests the forcing of the AAO by persistent ADP-like SIC anomalies. The leading time of SIC anomalies provides an implication for skillful predictability of springtime atmospheric variability.
Abstract
A lagged maximum covariance analysis (MCA) is applied to investigate the linear covariability between monthly sea ice concentration (SIC) and 500-mb geopotential height (Z500) in the Southern Hemisphere (SH). The dominant signal is the atmospheric forcing of SIC anomalies throughout the year, but statistically significant covariances are also found between austral springtime Z500 and prior SIC anomalies up to four months earlier. The MCA pattern is characterized by an Antarctic dipole (ADP)-like pattern in SIC and a positively polarized Antarctic Oscillation (AAO) in Z500. Such long lead-time covariance suggests the forcing of the AAO by persistent ADP-like SIC anomalies. The leading time of SIC anomalies provides an implication for skillful predictability of springtime atmospheric variability.
Abstract
Attribution studies conclude that it is extremely likely that most observed global- and continental-scale surface air temperature (SAT) warming since 1950 was caused by anthropogenic forcing, but some difficulties and uncertainties remain in attribution of warming in subcontinental regions and at time scales less than 50 years. This study uses global observations and CMIP5 simulations with various forcings, covering 1979–2005, and control runs to develop confidence intervals, to attribute regional trends of SAT and sea surface temperature (SST) to natural and anthropogenic causes.
Observations show warming, significantly different from natural variations at the 95% confidence level, over one-third of all grid boxes, and averaged over 15 of 21 subcontinental regions and 6 of 10 ocean basins. Coupled simulations forced with all forcing factors, or greenhouse gases only, reproduce observed SST and SAT patterns. Uncoupled AMIP-like atmosphere-only (prescribed SST and atmospheric radiative forcing) simulations reproduce observed SAT patterns. All of these simulations produce consistent net downward longwave radiation patterns. Simulations with natural-only forcing simulate weak warming. Anthropogenic forcing effects are clearly detectable at the 5% significance level at global, hemispheric, and tropical scales and in nine ocean basins and 15 of 21 subcontinental land regions. Attribution results indicate that ocean warming during 1979–2005 for the globe and individual basins is well represented in the CMIP5 multimodel ensemble mean historical simulations. While land warming may occur as an indirect response to oceanic warming, increasing greenhouse gas concentrations tend to be the ultimate source of land warming in most subcontinental regions during 1979–2005.
Abstract
Attribution studies conclude that it is extremely likely that most observed global- and continental-scale surface air temperature (SAT) warming since 1950 was caused by anthropogenic forcing, but some difficulties and uncertainties remain in attribution of warming in subcontinental regions and at time scales less than 50 years. This study uses global observations and CMIP5 simulations with various forcings, covering 1979–2005, and control runs to develop confidence intervals, to attribute regional trends of SAT and sea surface temperature (SST) to natural and anthropogenic causes.
Observations show warming, significantly different from natural variations at the 95% confidence level, over one-third of all grid boxes, and averaged over 15 of 21 subcontinental regions and 6 of 10 ocean basins. Coupled simulations forced with all forcing factors, or greenhouse gases only, reproduce observed SST and SAT patterns. Uncoupled AMIP-like atmosphere-only (prescribed SST and atmospheric radiative forcing) simulations reproduce observed SAT patterns. All of these simulations produce consistent net downward longwave radiation patterns. Simulations with natural-only forcing simulate weak warming. Anthropogenic forcing effects are clearly detectable at the 5% significance level at global, hemispheric, and tropical scales and in nine ocean basins and 15 of 21 subcontinental land regions. Attribution results indicate that ocean warming during 1979–2005 for the globe and individual basins is well represented in the CMIP5 multimodel ensemble mean historical simulations. While land warming may occur as an indirect response to oceanic warming, increasing greenhouse gas concentrations tend to be the ultimate source of land warming in most subcontinental regions during 1979–2005.
Abstract
The linear trends for a number of fields obtained from the reanalyses of the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) are calculated for the Northern Hemisphere winter months (January–March) from the 55-yr period of 1948–2002. The fields include sea level pressure (SLP); geopotential height at 500 and 50 hPa; temperature at 500 and 50 hPa; zonally averaged height; temperature; zonal, meridional, and vertical velocities from 1000 to 50 hPa; and surface air temperature (SAT). The trend fields are expressed in terms of two alternate expansions: (i) contributions from the Arctic Oscillation (AO) and cold ocean–warm land (COWL) patterns, as defined from the leading modes of an empirical orthogonal function (EOF) analysis of sea level pressure; or (ii) contributions from the modified AO (AO*) and modified COWL (COWL*) patterns, defined from the leading EOFs of 500-hPa height. The residuals in each expansion are considered, and the completeness properties of the expansions are discussed.
Long-term linear trends of various fields at mid- and lower-tropospheric levels project well onto the AO (AO*) and COWL (COWL*) modes. The AO contribution accounts for most of the SLP falls over the Arctic and half of the SLP rise over the North Atlantic, while the COWL pattern represents the entire negative pressure trend over the Pacific and half of the rise over the Atlantic. In the expansion into AO* and COWL* patterns, the latter represents most of the SLP trend. Similar remarks hold for the height trend at 500 hPa. In each case the residual is a small fraction of the trend. The observed SAT trend (warming over most of North America and Asia, cooling over northeast Canada and the Pacific) is partitioned nearly equally between contributions from the AO and COWL, although the COWL contribution dominates over North America. In the alternate expansion, the COWL* dominates nearly all of the warming over North America and Asia. The midtropospheric (500 hPa) temperature trend is mostly due to the COWL (or COWL*) patterns, with the AO representing only the local cooling over Greenland.
The 50-hPa height and temperature trends are not well represented by either set of patterns. The links of the trends in the zonal-mean fields and the AO (AO*) and COWL (COWL*) are weaker than those in the mid- and lower troposphere.
Abstract
The linear trends for a number of fields obtained from the reanalyses of the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) are calculated for the Northern Hemisphere winter months (January–March) from the 55-yr period of 1948–2002. The fields include sea level pressure (SLP); geopotential height at 500 and 50 hPa; temperature at 500 and 50 hPa; zonally averaged height; temperature; zonal, meridional, and vertical velocities from 1000 to 50 hPa; and surface air temperature (SAT). The trend fields are expressed in terms of two alternate expansions: (i) contributions from the Arctic Oscillation (AO) and cold ocean–warm land (COWL) patterns, as defined from the leading modes of an empirical orthogonal function (EOF) analysis of sea level pressure; or (ii) contributions from the modified AO (AO*) and modified COWL (COWL*) patterns, defined from the leading EOFs of 500-hPa height. The residuals in each expansion are considered, and the completeness properties of the expansions are discussed.
Long-term linear trends of various fields at mid- and lower-tropospheric levels project well onto the AO (AO*) and COWL (COWL*) modes. The AO contribution accounts for most of the SLP falls over the Arctic and half of the SLP rise over the North Atlantic, while the COWL pattern represents the entire negative pressure trend over the Pacific and half of the rise over the Atlantic. In the expansion into AO* and COWL* patterns, the latter represents most of the SLP trend. Similar remarks hold for the height trend at 500 hPa. In each case the residual is a small fraction of the trend. The observed SAT trend (warming over most of North America and Asia, cooling over northeast Canada and the Pacific) is partitioned nearly equally between contributions from the AO and COWL, although the COWL contribution dominates over North America. In the alternate expansion, the COWL* dominates nearly all of the warming over North America and Asia. The midtropospheric (500 hPa) temperature trend is mostly due to the COWL (or COWL*) patterns, with the AO representing only the local cooling over Greenland.
The 50-hPa height and temperature trends are not well represented by either set of patterns. The links of the trends in the zonal-mean fields and the AO (AO*) and COWL (COWL*) are weaker than those in the mid- and lower troposphere.
Abstract
Many climatic and geophysical processes are cyclostationary and exhibit appreciable cyclic (monthly, daily, etc.) variation of their statistics in addition to interannual fluctuations. Utilization of this nested variation of statistics will lead to a better chance of detecting a signal in such a varying background noise field, especially when the signal is strongly phase locked with the nested cycle. In this study, a detection technique is constructed in terms of cyclostationary empirical orthogonal functions, which take the nested periodicity of noise statistics into account. To investigate the improved performance of the cyclostationary approach the developed algorithm is applied to three specific detection examples: El Niño, greenhouse warming, and sunspot fluctuations. In all the test cases, signal-to-noise ratio is raised between 2% and 43% compared with that of a stationary detection technique. The variation of signal strength when a detection filter is constructed based on a different section of modeled noise is within the range of mean signal-to-noise ratio for small to moderate signals. There is a significant variation, however, of signal strength when a detection filter is constructed based on a different model dataset. This implies that model discrepancy is a more important factor than sampling error for the accuracy of the detection method and that climate models need to be improved further in their noise statistics.
Abstract
Many climatic and geophysical processes are cyclostationary and exhibit appreciable cyclic (monthly, daily, etc.) variation of their statistics in addition to interannual fluctuations. Utilization of this nested variation of statistics will lead to a better chance of detecting a signal in such a varying background noise field, especially when the signal is strongly phase locked with the nested cycle. In this study, a detection technique is constructed in terms of cyclostationary empirical orthogonal functions, which take the nested periodicity of noise statistics into account. To investigate the improved performance of the cyclostationary approach the developed algorithm is applied to three specific detection examples: El Niño, greenhouse warming, and sunspot fluctuations. In all the test cases, signal-to-noise ratio is raised between 2% and 43% compared with that of a stationary detection technique. The variation of signal strength when a detection filter is constructed based on a different section of modeled noise is within the range of mean signal-to-noise ratio for small to moderate signals. There is a significant variation, however, of signal strength when a detection filter is constructed based on a different model dataset. This implies that model discrepancy is a more important factor than sampling error for the accuracy of the detection method and that climate models need to be improved further in their noise statistics.
Abstract
Estimates of the amplitudes of the forced responses of the surface temperature field over the last century are provided by a signal processing scheme utilizing space–time empirical orthogonal functions for several combinations of station sites and record intervals taken from the last century. These century-long signal fingerprints come mainly from energy balance model calculations, which are shown to be very close to smoothed ensemble average runs from a coupled ocean–atmosphere model (Hadley Centre Model). The space–time lagged covariance matrices of natural variability come from 100-yr control runs from several well-known coupled ocean–atmosphere models as well as a 10 000-yr run from the stochastic energy balance climate model (EBCM). Evidence is found for robust, but weaker than expected signals from the greenhouse [amplitude ∼65% of that expected for a rather insensitive model (EBCM:
Abstract
Estimates of the amplitudes of the forced responses of the surface temperature field over the last century are provided by a signal processing scheme utilizing space–time empirical orthogonal functions for several combinations of station sites and record intervals taken from the last century. These century-long signal fingerprints come mainly from energy balance model calculations, which are shown to be very close to smoothed ensemble average runs from a coupled ocean–atmosphere model (Hadley Centre Model). The space–time lagged covariance matrices of natural variability come from 100-yr control runs from several well-known coupled ocean–atmosphere models as well as a 10 000-yr run from the stochastic energy balance climate model (EBCM). Evidence is found for robust, but weaker than expected signals from the greenhouse [amplitude ∼65% of that expected for a rather insensitive model (EBCM:
Abstract
The impact of the Eurasian snow cover extent on the Northern Hemisphere (NH) circulation is investigated by applying a lagged maximum covariance analysis (MCA) to monthly satellite-derived snow cover and NCEP reanalysis data. Wintertime atmospheric signals significantly correlated with persistently autumn–early winter snow cover anomalies are found in the leading two MCA modes. The first MCA mode indicates the effect of Eurasian snow cover anomalies on the Arctic Oscillation/North Atlantic Oscillation (AO/NAO). The second MCA mode corresponds with the forcing of Eurasian snow cover anomalies on the hemispheric Pacific–North America (PNA)-like atmospheric variations. This snow–atmosphere relationship may present a significant potential for wintertime predictability.
Abstract
The impact of the Eurasian snow cover extent on the Northern Hemisphere (NH) circulation is investigated by applying a lagged maximum covariance analysis (MCA) to monthly satellite-derived snow cover and NCEP reanalysis data. Wintertime atmospheric signals significantly correlated with persistently autumn–early winter snow cover anomalies are found in the leading two MCA modes. The first MCA mode indicates the effect of Eurasian snow cover anomalies on the Arctic Oscillation/North Atlantic Oscillation (AO/NAO). The second MCA mode corresponds with the forcing of Eurasian snow cover anomalies on the hemispheric Pacific–North America (PNA)-like atmospheric variations. This snow–atmosphere relationship may present a significant potential for wintertime predictability.
Abstract
Using pentad data of the Northern Hemisphere extended winter (November–March) from 1979 to 2012 derived from the daily rainfall of the National Meteorological Information Center of China, subseasonal variability of precipitation in China is analyzed. The two dominant modes of subseasonal variability are identified with an empirical orthogonal function (EOF) analysis. The first EOF mode (EOF1) is characterized by a monopole in South China, whereas the second EOF mode (EOF2) has a meridional dipole structure with opposite precipitation anomalies over the Yangtze River basin and the coastal area of South China. These two modes tend to have a phase shift to each other in both space and time, indicating that part of their variability represents a southward-propagating pattern.
The subseasonal variability is decomposed into two components: one related to the Madden–Julian oscillation (MJO) and the other independent of MJO. It is found that the MJO contributes to about 10% of the precipitation variability in South China. EOF1 is associated with MJO phase 3, corresponding to enhanced equatorial convection in the Indian Ocean and depressed convection in the western Pacific, while EOF2 is related to MJO phase 5 when the enhanced tropical convection moves to the Maritime Continent region. Subseasonal precipitation variability in China that is independent of the MJO is especially affected by processes including tropical convection variability and the “cold surge” phenomenon or the development of a Siberian high and cold-air outbreak in East Asia associated with a wave train from the North Atlantic.
Abstract
Using pentad data of the Northern Hemisphere extended winter (November–March) from 1979 to 2012 derived from the daily rainfall of the National Meteorological Information Center of China, subseasonal variability of precipitation in China is analyzed. The two dominant modes of subseasonal variability are identified with an empirical orthogonal function (EOF) analysis. The first EOF mode (EOF1) is characterized by a monopole in South China, whereas the second EOF mode (EOF2) has a meridional dipole structure with opposite precipitation anomalies over the Yangtze River basin and the coastal area of South China. These two modes tend to have a phase shift to each other in both space and time, indicating that part of their variability represents a southward-propagating pattern.
The subseasonal variability is decomposed into two components: one related to the Madden–Julian oscillation (MJO) and the other independent of MJO. It is found that the MJO contributes to about 10% of the precipitation variability in South China. EOF1 is associated with MJO phase 3, corresponding to enhanced equatorial convection in the Indian Ocean and depressed convection in the western Pacific, while EOF2 is related to MJO phase 5 when the enhanced tropical convection moves to the Maritime Continent region. Subseasonal precipitation variability in China that is independent of the MJO is especially affected by processes including tropical convection variability and the “cold surge” phenomenon or the development of a Siberian high and cold-air outbreak in East Asia associated with a wave train from the North Atlantic.
Abstract
Enhanced surface melt over the ice shelves of the Antarctic Peninsula (AP) is one of the precursors to their collapse, which can be proceeded by accelerated ground glacier flow and increased contribution to sea level rise. With the collapse of Larsen A and B and the major 2017 calving event from Larsen C, whether Larsen C is bound for a similar fate has received increasing attention. Here, the interannual variation of regional circulation over the AP region is studied using the empirical orthogonal function (EOF)/principal component (PC) analysis on the sea level pressure of ERA5. The EOF modes capture the variations of depth, location, and extent of Amundsen Sea low and Weddell Sea low in each season. Statistically significant positive correlations exist between Larsen C surface temperature and the PC time series of EOF mode 1 in winter and spring through northerly/northwesterly wind anomalies west of the AP. The PC time series of EOF mode 2 is negatively correlated with Larsen C surface temperature in autumn and summer and surface melt in summer, all due to southerly wind anomalies east of the AP. Surface energy budget analysis associated with EOF mode 2 shows that downwelling longwave radiation over Larsen C has negative statistically significant correlations with EOF mode 2 and is the major atmospheric forcing regulating the variation of Larsen C surface melt. Positively enhanced EOF mode 2 since 2004 is responsible for the recent cooling and decline of surface melt over Larsen C.
Abstract
Enhanced surface melt over the ice shelves of the Antarctic Peninsula (AP) is one of the precursors to their collapse, which can be proceeded by accelerated ground glacier flow and increased contribution to sea level rise. With the collapse of Larsen A and B and the major 2017 calving event from Larsen C, whether Larsen C is bound for a similar fate has received increasing attention. Here, the interannual variation of regional circulation over the AP region is studied using the empirical orthogonal function (EOF)/principal component (PC) analysis on the sea level pressure of ERA5. The EOF modes capture the variations of depth, location, and extent of Amundsen Sea low and Weddell Sea low in each season. Statistically significant positive correlations exist between Larsen C surface temperature and the PC time series of EOF mode 1 in winter and spring through northerly/northwesterly wind anomalies west of the AP. The PC time series of EOF mode 2 is negatively correlated with Larsen C surface temperature in autumn and summer and surface melt in summer, all due to southerly wind anomalies east of the AP. Surface energy budget analysis associated with EOF mode 2 shows that downwelling longwave radiation over Larsen C has negative statistically significant correlations with EOF mode 2 and is the major atmospheric forcing regulating the variation of Larsen C surface melt. Positively enhanced EOF mode 2 since 2004 is responsible for the recent cooling and decline of surface melt over Larsen C.
Abstract
The mei-yu onset over the middle to lower reaches of the Yangtze River Valley (MLYRV) varies considerably from early June to mid-July, which leads to large interannual changes in rainy-season length, total summer rainfall, and flooding potential. Previous studies have investigated the impact of El Niño–Southern Oscillation (ENSO) on the mei-yu onset. This study shows that a strong (weak) East Asian and western North Pacific (EAWNP) intraseasonal oscillation (ISO) in spring leads to an early (late) onset of the mei-yu over the MLYRV, and this ISO–mei-yu relationship is attributed to different types of ENSO in the preceding winter. A strong EAWNP ISO in spring is related to an eastern Pacific El Niño (EP El Niño) in the previous winter, and negative sea surface temperature (SST) anomalies in the eastern Indian Ocean and the South China Sea (SCS) in May, which can cause an early onset of the South China Sea summer monsoon that also favors an early mei-yu onset. In contrast, a weak EAWNP ISO in spring is associated with a central Pacific El Niño (CP El Niño) before April, but with an EP El Niño after April, and positive SST anomalies in both the eastern Indian Ocean and the SCS in May. A statistical forecast model combining the intensity of spring EAWNP ISO, CP ENSO, and EP ENSO indices shows a high prediction skill of the observed mei-yu onset date.
Abstract
The mei-yu onset over the middle to lower reaches of the Yangtze River Valley (MLYRV) varies considerably from early June to mid-July, which leads to large interannual changes in rainy-season length, total summer rainfall, and flooding potential. Previous studies have investigated the impact of El Niño–Southern Oscillation (ENSO) on the mei-yu onset. This study shows that a strong (weak) East Asian and western North Pacific (EAWNP) intraseasonal oscillation (ISO) in spring leads to an early (late) onset of the mei-yu over the MLYRV, and this ISO–mei-yu relationship is attributed to different types of ENSO in the preceding winter. A strong EAWNP ISO in spring is related to an eastern Pacific El Niño (EP El Niño) in the previous winter, and negative sea surface temperature (SST) anomalies in the eastern Indian Ocean and the South China Sea (SCS) in May, which can cause an early onset of the South China Sea summer monsoon that also favors an early mei-yu onset. In contrast, a weak EAWNP ISO in spring is associated with a central Pacific El Niño (CP El Niño) before April, but with an EP El Niño after April, and positive SST anomalies in both the eastern Indian Ocean and the SCS in May. A statistical forecast model combining the intensity of spring EAWNP ISO, CP ENSO, and EP ENSO indices shows a high prediction skill of the observed mei-yu onset date.
