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- Author or Editor: Chris K. Folland x
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Abstract
It is shown that a number of important characteristics of the global atmospheric circulation and climate changed in a near-monotonic fashion over the decade, or less, centered on the late 1960s. These changes were largest or commonest in tropical regions, the Southern Hemisphere, and the Atlantic sector of the Northern Hemisphere. Some, such as the decrease in rainfall in the African Sahel, are well known. Others appear to be new, but their combined extent is global and dynamical linkages between them are evident. The list of affected variables includes patterns of SST; tropical rainfall in the African Sahel and Sudan, the Amazon basin, and northeast Brazil; pressure and SST in the tropical North Atlantic and the west and central Pacific; various branches of the southern Hadley circulation and the southern subtropical jet stream; the summer North Atlantic Oscillation; south Greenland temperature; the Southern Hemisphere storm track; and, quite likely, the Antarctic sea ice boundary. These changes are often strongest in the June–August season; changes are also seen in December–February but are generally smaller. In Greenland, annual mean temperature seems to be affected strongly, reflecting similar changes in SST throughout the year in the higher latitudes of the North Atlantic. Possible causes for these coordinated changes are briefly evaluated. The most likely candidates appear to be a likely reduction in the northward oceanic heat flux associated with the North Atlantic thermohaline circulation in the 1950s to 1970s, which was nearly in phase with a rapid increase in anthropogenic aerosol emissions during the 1950s and 1960s, particularly over Europe and North America.
Abstract
It is shown that a number of important characteristics of the global atmospheric circulation and climate changed in a near-monotonic fashion over the decade, or less, centered on the late 1960s. These changes were largest or commonest in tropical regions, the Southern Hemisphere, and the Atlantic sector of the Northern Hemisphere. Some, such as the decrease in rainfall in the African Sahel, are well known. Others appear to be new, but their combined extent is global and dynamical linkages between them are evident. The list of affected variables includes patterns of SST; tropical rainfall in the African Sahel and Sudan, the Amazon basin, and northeast Brazil; pressure and SST in the tropical North Atlantic and the west and central Pacific; various branches of the southern Hadley circulation and the southern subtropical jet stream; the summer North Atlantic Oscillation; south Greenland temperature; the Southern Hemisphere storm track; and, quite likely, the Antarctic sea ice boundary. These changes are often strongest in the June–August season; changes are also seen in December–February but are generally smaller. In Greenland, annual mean temperature seems to be affected strongly, reflecting similar changes in SST throughout the year in the higher latitudes of the North Atlantic. Possible causes for these coordinated changes are briefly evaluated. The most likely candidates appear to be a likely reduction in the northward oceanic heat flux associated with the North Atlantic thermohaline circulation in the 1950s to 1970s, which was nearly in phase with a rapid increase in anthropogenic aerosol emissions during the 1950s and 1960s, particularly over Europe and North America.
Abstract
A study of the impact of ENSO in the Hadley Centre’s atmospheric climate model HADAM1 is presented, with emphasis on the North Pacific–American (NPA) sector. The study is based both on observational data and an ensemble of six integrations for the period 1949–93, forced with observed global sea-ice and sea surface temperature data. The model is shown to reproduce most of the known features of the worldwide atmospheric response to ENSO in boreal winter (January–March).
Focusing on the NPA sector, the leading modes of low-frequency weather variability in the winter season are identified on their natural timescales for both the modeled and observed atmospheres. These modes are analyzed via rotated EOF analysis of daily 500-hPa height data, filtered to remove synoptic timescale variations. The model gives a reasonably skillful simulation of the main features of the four leading modes in the NPA region:the Pacific–North American (PNA), the west Pacific (WP), the east Pacific (EP), and the North Pacific (NP) modes. The sensitivity of these modes to SSTs is investigated. In particular, sensitivity to SSTs associated with ENSO is analyzed in terms of the shift in frequency of occurrence of the opposing phases of a mode between warm event (El Niño) and cold event (La Niña) years. Three of the observed modes show such a sensitivity: the PNA, WP, and NP modes. Of the corresponding model modes, only the PNA responds significantly to ENSO (but too strongly in warm event years), which is clearly illustrated by changes in both the frequency and duration of PNA episodes between warm and cold event years. The EP mode shows no sensitivity to ENSO, in either model or observed atmospheres. Finally, although the model is able to reproduce the pattern of decadal anomalies seen in the North Pacific in the years 1977–87, which is related to the prevalence of the positive phase of the PNA in this period, it does so with a much reduced amplitude; possible reasons for this discrepancy are discussed.
Abstract
A study of the impact of ENSO in the Hadley Centre’s atmospheric climate model HADAM1 is presented, with emphasis on the North Pacific–American (NPA) sector. The study is based both on observational data and an ensemble of six integrations for the period 1949–93, forced with observed global sea-ice and sea surface temperature data. The model is shown to reproduce most of the known features of the worldwide atmospheric response to ENSO in boreal winter (January–March).
Focusing on the NPA sector, the leading modes of low-frequency weather variability in the winter season are identified on their natural timescales for both the modeled and observed atmospheres. These modes are analyzed via rotated EOF analysis of daily 500-hPa height data, filtered to remove synoptic timescale variations. The model gives a reasonably skillful simulation of the main features of the four leading modes in the NPA region:the Pacific–North American (PNA), the west Pacific (WP), the east Pacific (EP), and the North Pacific (NP) modes. The sensitivity of these modes to SSTs is investigated. In particular, sensitivity to SSTs associated with ENSO is analyzed in terms of the shift in frequency of occurrence of the opposing phases of a mode between warm event (El Niño) and cold event (La Niña) years. Three of the observed modes show such a sensitivity: the PNA, WP, and NP modes. Of the corresponding model modes, only the PNA responds significantly to ENSO (but too strongly in warm event years), which is clearly illustrated by changes in both the frequency and duration of PNA episodes between warm and cold event years. The EP mode shows no sensitivity to ENSO, in either model or observed atmospheres. Finally, although the model is able to reproduce the pattern of decadal anomalies seen in the North Pacific in the years 1977–87, which is related to the prevalence of the positive phase of the PNA in this period, it does so with a much reduced amplitude; possible reasons for this discrepancy are discussed.
Abstract
The predictability of rainy season rainfall over northeast Brazil for the relatively long period 1912–98 is analyzed using dynamical and empirical techniques. The dynamical assessments are based on the HadAM2b atmospheric model forced with the Met Office Global Sea Ice and Sea Surface Temperature Dataset (GISST3). Ensembles of simulations and hindcasts starting from real initial conditions for 1982–93 made under the European Community Prediction of Climate Variations on Seasonal to Interannual Timescales (PROVOST) program are analyzed. The results demonstrate a relatively high degree of predictability. Its source lies mostly in tropical Atlantic and Pacific sea surface temperatures. The results confirm the less extensive evidence of other authors that northeast Brazil is a region where two separate ocean basins influence seasonal climate to a comparable extent. Overall, the sea surface temperature gradient between the northern and southern tropical Atlantic appears to be the more important influence, though El Niño can be dominant when it is strong. These assessments of predictability are consistent with the performance of over a decade of real-time long lead and updated forecasts, issued over the period 1987–98. Multiple regression and linear discriminant analysis prediction techniques, together with model forecasts in the last few years, were used to provide best estimate and probability real-time forecasts of rainy season rainfall. These forecasts had a level of skill that was close to the state of the art in seasonal forecasting
Abstract
The predictability of rainy season rainfall over northeast Brazil for the relatively long period 1912–98 is analyzed using dynamical and empirical techniques. The dynamical assessments are based on the HadAM2b atmospheric model forced with the Met Office Global Sea Ice and Sea Surface Temperature Dataset (GISST3). Ensembles of simulations and hindcasts starting from real initial conditions for 1982–93 made under the European Community Prediction of Climate Variations on Seasonal to Interannual Timescales (PROVOST) program are analyzed. The results demonstrate a relatively high degree of predictability. Its source lies mostly in tropical Atlantic and Pacific sea surface temperatures. The results confirm the less extensive evidence of other authors that northeast Brazil is a region where two separate ocean basins influence seasonal climate to a comparable extent. Overall, the sea surface temperature gradient between the northern and southern tropical Atlantic appears to be the more important influence, though El Niño can be dominant when it is strong. These assessments of predictability are consistent with the performance of over a decade of real-time long lead and updated forecasts, issued over the period 1987–98. Multiple regression and linear discriminant analysis prediction techniques, together with model forecasts in the last few years, were used to provide best estimate and probability real-time forecasts of rainy season rainfall. These forecasts had a level of skill that was close to the state of the art in seasonal forecasting
Abstract
Presented herein is an experimental design that allows the effects of several radiative forcing factors on climate to be estimated as precisely as possible from a limited suite of atmosphere-only general circulation model (GCM) integrations. The forcings include the combined effect of observed changes in sea surface temperatures, sea ice extent, stratospheric (volcanic) aerosols, and solar output, plus the individual effects of several anthropogenic forcings. A single linear statistical model is used to estimate the forcing effects, each of which is represented by its global mean radiative forcing. The strong colinearity in time between the various anthropogenic forcings provides a technical problem that is overcome through the design of the experiment. This design uses every combination of anthropogenic forcing rather than having a few highly replicated ensembles, which is more commonly used in climate studies. Not only is this design highly efficient for a given number of integrations, but it also allows the estimation of (nonadditive) interactions between pairs of anthropogenic forcings.
The simulated land surface air temperature changes since 1871 have been analyzed. The changes in natural and oceanic forcing, which itself contains some forcing from anthropogenic and natural influences, have the most influence. For the global mean, increasing greenhouse gases and the indirect aerosol effect had the largest anthropogenic effects. It was also found that an interaction between these two anthropogenic effects in the atmosphere-only GCM exists. This interaction is similar in magnitude to the individual effects of changing tropospheric and stratospheric ozone concentrations or to the direct (sulfate) aerosol effect. Various diagnostics are used to evaluate the fit of the statistical model. For the global mean, this shows that the land temperature response is proportional to the global mean radiative forcing, reinforcing the use of radiative forcing as a measure of climate change. The diagnostic tests also show that the linear model was suitable for analyses of land surface air temperature at each GCM grid point. Therefore, the linear model provides precise estimates of the space–time signals for all forcing factors under consideration. For simulated 50-hPa temperatures, results show that tropospheric ozone increases have contributed to stratospheric cooling over the twentieth century almost as much as changes in well-mixed greenhouse gases.
Abstract
Presented herein is an experimental design that allows the effects of several radiative forcing factors on climate to be estimated as precisely as possible from a limited suite of atmosphere-only general circulation model (GCM) integrations. The forcings include the combined effect of observed changes in sea surface temperatures, sea ice extent, stratospheric (volcanic) aerosols, and solar output, plus the individual effects of several anthropogenic forcings. A single linear statistical model is used to estimate the forcing effects, each of which is represented by its global mean radiative forcing. The strong colinearity in time between the various anthropogenic forcings provides a technical problem that is overcome through the design of the experiment. This design uses every combination of anthropogenic forcing rather than having a few highly replicated ensembles, which is more commonly used in climate studies. Not only is this design highly efficient for a given number of integrations, but it also allows the estimation of (nonadditive) interactions between pairs of anthropogenic forcings.
The simulated land surface air temperature changes since 1871 have been analyzed. The changes in natural and oceanic forcing, which itself contains some forcing from anthropogenic and natural influences, have the most influence. For the global mean, increasing greenhouse gases and the indirect aerosol effect had the largest anthropogenic effects. It was also found that an interaction between these two anthropogenic effects in the atmosphere-only GCM exists. This interaction is similar in magnitude to the individual effects of changing tropospheric and stratospheric ozone concentrations or to the direct (sulfate) aerosol effect. Various diagnostics are used to evaluate the fit of the statistical model. For the global mean, this shows that the land temperature response is proportional to the global mean radiative forcing, reinforcing the use of radiative forcing as a measure of climate change. The diagnostic tests also show that the linear model was suitable for analyses of land surface air temperature at each GCM grid point. Therefore, the linear model provides precise estimates of the space–time signals for all forcing factors under consideration. For simulated 50-hPa temperatures, results show that tropospheric ozone increases have contributed to stratospheric cooling over the twentieth century almost as much as changes in well-mixed greenhouse gases.
Abstract
Gridded trends of annual values of various climate extreme indices were estimated for 1950 to 1995, presenting a clearer picture of the patterns of trends in climate extremes than has been seen with raw station data. The gridding also allows one, for the first time, to compare these observed trends with those simulated by a suite of climate model runs forced by observed changes in sea surface temperatures, sea ice extent, and various combinations of human-induced forcings.
Bootstrapping techniques are used to assess the uncertainty in the gridded trend estimates and the field significance of the patterns of observed trends. The findings mainly confirm earlier, less objectively derived, results based on station data. There have been significant decreases in the number of frost days and increases in the number of very warm nights over much of the Northern Hemisphere. Regions of significant increases in rainfall extremes and decreases in the number of consecutive dry days are smaller in extent. However, patterns of trends in annual maximum 5-day rainfall totals were not significant.
Comparisons of the observed trend estimates with those simulated by the climate model indicate that the inclusion of anthropogenic effects in the model integrations, in particular increasing greenhouse gases, significantly improves the simulation of changing extremes in temperatures. This analysis provides good evidence that human-induced forcing has recently played an important role in extreme climate. The model shows little skill in simulating changing precipitation extremes.
Abstract
Gridded trends of annual values of various climate extreme indices were estimated for 1950 to 1995, presenting a clearer picture of the patterns of trends in climate extremes than has been seen with raw station data. The gridding also allows one, for the first time, to compare these observed trends with those simulated by a suite of climate model runs forced by observed changes in sea surface temperatures, sea ice extent, and various combinations of human-induced forcings.
Bootstrapping techniques are used to assess the uncertainty in the gridded trend estimates and the field significance of the patterns of observed trends. The findings mainly confirm earlier, less objectively derived, results based on station data. There have been significant decreases in the number of frost days and increases in the number of very warm nights over much of the Northern Hemisphere. Regions of significant increases in rainfall extremes and decreases in the number of consecutive dry days are smaller in extent. However, patterns of trends in annual maximum 5-day rainfall totals were not significant.
Comparisons of the observed trend estimates with those simulated by the climate model indicate that the inclusion of anthropogenic effects in the model integrations, in particular increasing greenhouse gases, significantly improves the simulation of changing extremes in temperatures. This analysis provides good evidence that human-induced forcing has recently played an important role in extreme climate. The model shows little skill in simulating changing precipitation extremes.
Abstract
The authors estimate the change in extreme winter weather events over Europe that is due to a long-term change in the North Atlantic Oscillation (NAO) such as that observed between the 1960s and 1990s. Using ensembles of simulations from a general circulation model, large changes in the frequency of 10th percentile temperature and 90th percentile precipitation events over Europe are found from changes in the NAO. In some cases, these changes are comparable to the expected change in the frequency of events due to anthropogenic forcing over the twenty-first century. Although the results presented here do not affect anthropogenic interpretation of global and annual mean changes in observed extremes, they do show that great care is needed to assess changes due to modes of climate variability when interpreting extreme events on regional and seasonal scales. How changes in natural modes of variability, such as the NAO, could radically alter current climate model predictions of changes in extreme weather events on multidecadal time scales is also discussed.
Abstract
The authors estimate the change in extreme winter weather events over Europe that is due to a long-term change in the North Atlantic Oscillation (NAO) such as that observed between the 1960s and 1990s. Using ensembles of simulations from a general circulation model, large changes in the frequency of 10th percentile temperature and 90th percentile precipitation events over Europe are found from changes in the NAO. In some cases, these changes are comparable to the expected change in the frequency of events due to anthropogenic forcing over the twenty-first century. Although the results presented here do not affect anthropogenic interpretation of global and annual mean changes in observed extremes, they do show that great care is needed to assess changes due to modes of climate variability when interpreting extreme events on regional and seasonal scales. How changes in natural modes of variability, such as the NAO, could radically alter current climate model predictions of changes in extreme weather events on multidecadal time scales is also discussed.
Abstract
Summer climate in the North Atlantic–European sector possesses a principal pattern of year-to-year variability that is the parallel to the well-known North Atlantic Oscillation in winter. This summer North Atlantic Oscillation (SNAO) is defined here as the first empirical orthogonal function (EOF) of observed summertime extratropical North Atlantic pressure at mean sea level. It is shown to be characterized by a more northerly location and smaller spatial scale than its winter counterpart. The SNAO is also detected by cluster analysis and has a near-equivalent barotropic structure on daily and monthly time scales. Although of lesser amplitude than its wintertime counterpart, the SNAO exerts a strong influence on northern European rainfall, temperature, and cloudiness through changes in the position of the North Atlantic storm track. It is, therefore, of key importance in generating summer climate extremes, including flooding, drought, and heat stress in northwestern Europe. The El Niño–Southern Oscillation (ENSO) phenomenon is known to influence summertime European climate; however, interannual variations of the SNAO are only weakly influenced by ENSO. On interdecadal time scales, both modeling and observational results indicate that SNAO variations are partly related to the Atlantic multidecadal oscillation. It is shown that SNAO variations extend far back in time, as evidenced by reconstructions of SNAO variations back to 1706 using tree-ring records. Very long instrumental records, such as central England temperature, are used to validate the reconstruction. Finally, two climate models are shown to simulate the present-day SNAO and predict a trend toward a more positive index phase in the future under increasing greenhouse gas concentrations. This implies the long-term likelihood of increased summer drought for northwestern Europe.
Abstract
Summer climate in the North Atlantic–European sector possesses a principal pattern of year-to-year variability that is the parallel to the well-known North Atlantic Oscillation in winter. This summer North Atlantic Oscillation (SNAO) is defined here as the first empirical orthogonal function (EOF) of observed summertime extratropical North Atlantic pressure at mean sea level. It is shown to be characterized by a more northerly location and smaller spatial scale than its winter counterpart. The SNAO is also detected by cluster analysis and has a near-equivalent barotropic structure on daily and monthly time scales. Although of lesser amplitude than its wintertime counterpart, the SNAO exerts a strong influence on northern European rainfall, temperature, and cloudiness through changes in the position of the North Atlantic storm track. It is, therefore, of key importance in generating summer climate extremes, including flooding, drought, and heat stress in northwestern Europe. The El Niño–Southern Oscillation (ENSO) phenomenon is known to influence summertime European climate; however, interannual variations of the SNAO are only weakly influenced by ENSO. On interdecadal time scales, both modeling and observational results indicate that SNAO variations are partly related to the Atlantic multidecadal oscillation. It is shown that SNAO variations extend far back in time, as evidenced by reconstructions of SNAO variations back to 1706 using tree-ring records. Very long instrumental records, such as central England temperature, are used to validate the reconstruction. Finally, two climate models are shown to simulate the present-day SNAO and predict a trend toward a more positive index phase in the future under increasing greenhouse gas concentrations. This implies the long-term likelihood of increased summer drought for northwestern Europe.
Abstract
Australian rainfall variability and its relationship with the Southern Oscillation index (SOI) and global sea surface temperature (SST) variability is considered in both observational datasets and ensembles of multidecadal simulations using two different atmospheric general circulation models forced by observed SSTs and sea ice extent. Monthly and seasonal time series have been constructed to examine the observed and modeled relationships.
The models show some success in the Australian region, largely reproducing the observed relationships between rainfall, the SOI, and global SSTs, albeit better in some seasons and geographical regions than others. A partition of the rainfall variance into components due to SST forcing and internal variability, suggests that both models have too much internal variability over the central eastern half of the continent, especially during austral winter and spring. Consequently, the strength of the SOI and SST relationships tend to be underestimated in this region. The largest impact of SST forcing is seen over the tropical and western parts of the continent.
A principal component analysis reveals two dominant rotated modes of rainfall variability that are very similar in both the observed and modeled cases. One of these modes is significantly correlated with SST anomalies to the north-northwest of Australia (in the case of the models) and the SST gradient between the Indonesian archepelago and the central Indian Ocean (in the observed case). The other mode is significantly correlated with the typical SST anomaly pattern associated with the El Niño–Southern Oscillation. Correlative maps between the principal component time series and the modeled MSLP, 700-hPa, and 300-hPa geopotential heights are used to explore the underlying physical processes associated with these statistical relationships.
Abstract
Australian rainfall variability and its relationship with the Southern Oscillation index (SOI) and global sea surface temperature (SST) variability is considered in both observational datasets and ensembles of multidecadal simulations using two different atmospheric general circulation models forced by observed SSTs and sea ice extent. Monthly and seasonal time series have been constructed to examine the observed and modeled relationships.
The models show some success in the Australian region, largely reproducing the observed relationships between rainfall, the SOI, and global SSTs, albeit better in some seasons and geographical regions than others. A partition of the rainfall variance into components due to SST forcing and internal variability, suggests that both models have too much internal variability over the central eastern half of the continent, especially during austral winter and spring. Consequently, the strength of the SOI and SST relationships tend to be underestimated in this region. The largest impact of SST forcing is seen over the tropical and western parts of the continent.
A principal component analysis reveals two dominant rotated modes of rainfall variability that are very similar in both the observed and modeled cases. One of these modes is significantly correlated with SST anomalies to the north-northwest of Australia (in the case of the models) and the SST gradient between the Indonesian archepelago and the central Indian Ocean (in the observed case). The other mode is significantly correlated with the typical SST anomaly pattern associated with the El Niño–Southern Oscillation. Correlative maps between the principal component time series and the modeled MSLP, 700-hPa, and 300-hPa geopotential heights are used to explore the underlying physical processes associated with these statistical relationships.
