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Abstract
A set of idealized global model experiments was performed by several modeling centers as part of the Drought Working Group of the U.S. Climate Variability and Predictability component of the World Climate Research Programme (CLIVAR). The purpose of the experiments was to assess the role of the leading modes of sea surface temperature (SST) variability on the climate over the continents, with particular emphasis on the influence of SSTs on surface climate variability and droughts over the United States. An analysis based on several models gives more creditability to the results since it relies on the assessment of impacts that are robust across different models.
Coordinated atmospheric general circulation model (AGCM) simulations forced with three modes of SST variability were analyzed. The results show that the SST-forced precipitation variability over the central United States is dominated by the SST mode with maximum loading in the central Pacific Ocean. The SST mode with loading in the Atlantic Ocean, and a mode that is dominated by trends in SSTs, lead to a smaller response.
Based on the response to the idealized SSTs, the precipitation response for the twentieth century was also reconstructed. A comparison with the Atmospheric Model Intercomparison Project (AMIP) simulations forced with the observed SSTs illustrates that the reconstructed precipitation variability was similar to the one in the AMIP simulations, further supporting the conclusion that the SST modes identified in the present analysis play a dominant role in the precipitation variability over the United States. One notable exception is the Dust Bowl of the 1930s, and further analysis regarding this major climate extreme is discussed.
Abstract
A set of idealized global model experiments was performed by several modeling centers as part of the Drought Working Group of the U.S. Climate Variability and Predictability component of the World Climate Research Programme (CLIVAR). The purpose of the experiments was to assess the role of the leading modes of sea surface temperature (SST) variability on the climate over the continents, with particular emphasis on the influence of SSTs on surface climate variability and droughts over the United States. An analysis based on several models gives more creditability to the results since it relies on the assessment of impacts that are robust across different models.
Coordinated atmospheric general circulation model (AGCM) simulations forced with three modes of SST variability were analyzed. The results show that the SST-forced precipitation variability over the central United States is dominated by the SST mode with maximum loading in the central Pacific Ocean. The SST mode with loading in the Atlantic Ocean, and a mode that is dominated by trends in SSTs, lead to a smaller response.
Based on the response to the idealized SSTs, the precipitation response for the twentieth century was also reconstructed. A comparison with the Atmospheric Model Intercomparison Project (AMIP) simulations forced with the observed SSTs illustrates that the reconstructed precipitation variability was similar to the one in the AMIP simulations, further supporting the conclusion that the SST modes identified in the present analysis play a dominant role in the precipitation variability over the United States. One notable exception is the Dust Bowl of the 1930s, and further analysis regarding this major climate extreme is discussed.
Abstract
Recent studies demonstrate that ocean–atmosphere forcing by persistent sea surface temperature (SST) anomalies is a primary driver of seasonal-to-interannual hydroclimatic variability, including drought events. Other studies, however, conclude that although SST anomalies influence the timing of drought events, their duration and magnitude over continental regions is largely governed by land–atmosphere feedbacks. Here the authors evaluate the direct influence of SST anomalies on the stochastic characteristics of precipitation and drought in two ensembles of AGCM simulations forced with observed (interannually varying) monthly SST and their climatological annual cycle, respectively. Results demonstrate that ocean–atmosphere forcing contributes to the magnitude and persistence of simulated seasonal precipitation anomalies throughout the tropics but over few mid- and high-latitude regions. Significant autocorrelation of simulated seasonal anomalies over oceans is directly forced by persistent SST anomalies; over land, SST anomalies are shown to enhance autocorrelation associated with land–atmosphere feedbacks. SST anomalies are shown to have no significant influence on simulated drought frequency, duration, or magnitude over most midlatitude land regions. Results suggest that severe and sustained drought events may occur in the absence of persistent SST forcing and support recent conclusions that ocean–atmosphere forcing primarily influences the timing of drought events, while duration and magnitude are governed by other mechanisms such as land–atmosphere feedbacks. Further analysis is needed to assess the potential model dependence of results and to quantify the relative contribution of land–atmosphere feedbacks to the long-term stochastic characteristics of precipitation and drought.
Abstract
Recent studies demonstrate that ocean–atmosphere forcing by persistent sea surface temperature (SST) anomalies is a primary driver of seasonal-to-interannual hydroclimatic variability, including drought events. Other studies, however, conclude that although SST anomalies influence the timing of drought events, their duration and magnitude over continental regions is largely governed by land–atmosphere feedbacks. Here the authors evaluate the direct influence of SST anomalies on the stochastic characteristics of precipitation and drought in two ensembles of AGCM simulations forced with observed (interannually varying) monthly SST and their climatological annual cycle, respectively. Results demonstrate that ocean–atmosphere forcing contributes to the magnitude and persistence of simulated seasonal precipitation anomalies throughout the tropics but over few mid- and high-latitude regions. Significant autocorrelation of simulated seasonal anomalies over oceans is directly forced by persistent SST anomalies; over land, SST anomalies are shown to enhance autocorrelation associated with land–atmosphere feedbacks. SST anomalies are shown to have no significant influence on simulated drought frequency, duration, or magnitude over most midlatitude land regions. Results suggest that severe and sustained drought events may occur in the absence of persistent SST forcing and support recent conclusions that ocean–atmosphere forcing primarily influences the timing of drought events, while duration and magnitude are governed by other mechanisms such as land–atmosphere feedbacks. Further analysis is needed to assess the potential model dependence of results and to quantify the relative contribution of land–atmosphere feedbacks to the long-term stochastic characteristics of precipitation and drought.
Abstract
The U.S. Great Plains experienced a number of multiyear droughts during the last century, most notably the droughts of the 1930s and 1950s. This study examines the causes of such droughts using ensembles of long-term (1930–2000) simulations carried out with the NASA Seasonal-to-Interannual Prediction Project (NSIPP-1) atmospheric general circulation model (AGCM) forced with observed sea surface temperatures (SSTs). The results show that the model produces long-term (multiyear) variations in precipitation in the Great Plains region (30°–50°N, 95°–105°W) that are similar to those observed.
A correlative analysis suggests that the ensemble-mean low-frequency (time scales longer than about 6 yr) rainfall variations in the Great Plains are linked to a pan-Pacific pattern of SST variability that is the leading empirical orthogonal function (EOF) in the low-frequency SST data. The link between the SST and the Great Plains precipitation is confirmed in idealized AGCM simulations, in which the model is forced by the two polarities of the pan-Pacific SST pattern. The idealized simulations further show that it is primarily the tropical part of the SST anomalies that influences the Great Plains. As such, the Great Plains tend to have above-normal precipitation when the tropical Pacific SSTs are above normal, while there is a tendency for drought when the tropical SSTs are cold. The upper-tropospheric response to the pan-Pacific SST EOF shows a global-scale pattern with a strong wave response in the Pacific and a substantial zonally symmetric component in which U.S. Great Plains pluvial (drought) conditions are associated with reduced (enhanced) heights throughout the extratropics.
The potential predictability of rainfall in the Great Plains associated with SSTs is rather modest, with about one-third of the total low-frequency rainfall variance being forced by SST anomalies. Further idealized experiments with climatological SST suggest that the remaining low-frequency variance in the Great Plains precipitation is the result of interactions with soil moisture. In particular, simulations with soil moisture feedback show a fivefold increase in the variance in annual Great Plains precipitation compared with simulations in which the soil feedback is excluded. In addition to increasing variance, the interactions with the soil introduce a year-to-year memory in the hydrological cycle. The impact of soil memory is consistent with a red noise process, in which the deep soil is forced by white noise and damped with a time scale of about 1.5 yr. As such, the role of low-frequency SST variability is to introduce a bias to the net forcing on the soil moisture that drives the random process preferentially to either wet or dry conditions.
Abstract
The U.S. Great Plains experienced a number of multiyear droughts during the last century, most notably the droughts of the 1930s and 1950s. This study examines the causes of such droughts using ensembles of long-term (1930–2000) simulations carried out with the NASA Seasonal-to-Interannual Prediction Project (NSIPP-1) atmospheric general circulation model (AGCM) forced with observed sea surface temperatures (SSTs). The results show that the model produces long-term (multiyear) variations in precipitation in the Great Plains region (30°–50°N, 95°–105°W) that are similar to those observed.
A correlative analysis suggests that the ensemble-mean low-frequency (time scales longer than about 6 yr) rainfall variations in the Great Plains are linked to a pan-Pacific pattern of SST variability that is the leading empirical orthogonal function (EOF) in the low-frequency SST data. The link between the SST and the Great Plains precipitation is confirmed in idealized AGCM simulations, in which the model is forced by the two polarities of the pan-Pacific SST pattern. The idealized simulations further show that it is primarily the tropical part of the SST anomalies that influences the Great Plains. As such, the Great Plains tend to have above-normal precipitation when the tropical Pacific SSTs are above normal, while there is a tendency for drought when the tropical SSTs are cold. The upper-tropospheric response to the pan-Pacific SST EOF shows a global-scale pattern with a strong wave response in the Pacific and a substantial zonally symmetric component in which U.S. Great Plains pluvial (drought) conditions are associated with reduced (enhanced) heights throughout the extratropics.
The potential predictability of rainfall in the Great Plains associated with SSTs is rather modest, with about one-third of the total low-frequency rainfall variance being forced by SST anomalies. Further idealized experiments with climatological SST suggest that the remaining low-frequency variance in the Great Plains precipitation is the result of interactions with soil moisture. In particular, simulations with soil moisture feedback show a fivefold increase in the variance in annual Great Plains precipitation compared with simulations in which the soil feedback is excluded. In addition to increasing variance, the interactions with the soil introduce a year-to-year memory in the hydrological cycle. The impact of soil memory is consistent with a red noise process, in which the deep soil is forced by white noise and damped with a time scale of about 1.5 yr. As such, the role of low-frequency SST variability is to introduce a bias to the net forcing on the soil moisture that drives the random process preferentially to either wet or dry conditions.
Abstract
The land area surrounding the Mediterranean Sea has experienced 10 of the 12 driest winters since 1902 in just the last 20 years. A change in wintertime Mediterranean precipitation toward drier conditions has likely occurred over 1902–2010 whose magnitude cannot be reconciled with internal variability alone. Anthropogenic greenhouse gas and aerosol forcing are key attributable factors for this increased drying, though the external signal explains only half of the drying magnitude. Furthermore, sea surface temperature (SST) forcing during 1902–2010 likely played an important role in the observed Mediterranean drying, and the externally forced drying signal likely also occurs through an SST change signal.
The observed wintertime Mediterranean drying over the last century can be understood in a simple framework of the region’s sensitivity to a uniform global ocean warming and to modest changes in the ocean’s zonal and meridional SST gradients. Climate models subjected to a uniform +0.5°C warming of the world oceans induce eastern Mediterranean drying but fail to generate the observed widespread Mediterranean drying pattern. For a +0.5°C SST warming confined to tropical latitudes only, a dry signal spanning the entire Mediterranean region occurs. The simulated Mediterranean drying intensifies further when the Indian Ocean is warmed +0.5°C more than the remaining tropical oceans, an enhanced drying signal attributable to a distinctive atmospheric circulation response resembling the positive phase of the North Atlantic Oscillation. The extent to which these mechanisms and the region’s overall drying since 1902 reflect similar mechanisms operating in association with external radiative forcing are discussed.
Abstract
The land area surrounding the Mediterranean Sea has experienced 10 of the 12 driest winters since 1902 in just the last 20 years. A change in wintertime Mediterranean precipitation toward drier conditions has likely occurred over 1902–2010 whose magnitude cannot be reconciled with internal variability alone. Anthropogenic greenhouse gas and aerosol forcing are key attributable factors for this increased drying, though the external signal explains only half of the drying magnitude. Furthermore, sea surface temperature (SST) forcing during 1902–2010 likely played an important role in the observed Mediterranean drying, and the externally forced drying signal likely also occurs through an SST change signal.
The observed wintertime Mediterranean drying over the last century can be understood in a simple framework of the region’s sensitivity to a uniform global ocean warming and to modest changes in the ocean’s zonal and meridional SST gradients. Climate models subjected to a uniform +0.5°C warming of the world oceans induce eastern Mediterranean drying but fail to generate the observed widespread Mediterranean drying pattern. For a +0.5°C SST warming confined to tropical latitudes only, a dry signal spanning the entire Mediterranean region occurs. The simulated Mediterranean drying intensifies further when the Indian Ocean is warmed +0.5°C more than the remaining tropical oceans, an enhanced drying signal attributable to a distinctive atmospheric circulation response resembling the positive phase of the North Atlantic Oscillation. The extent to which these mechanisms and the region’s overall drying since 1902 reflect similar mechanisms operating in association with external radiative forcing are discussed.
Abstract
The atmospheric and land components of the Geophysical Fluid Dynamics Laboratory’s (GFDL’s) Climate Model version 2.1 (CM2.1) is used with climatological sea surface temperatures (SSTs) to investigate the relative climatic impacts of historical anthropogenic land cover change (LCC) and realistic SST anomalies. The SST forcing anomalies used are analogous to signals induced by El Niño–Southern Oscillation (ENSO), the North Atlantic Oscillation (NAO), and the background global warming trend. Coherent areas of LCC are represented throughout much of central and eastern Europe, northern India, southeastern China, and on either side of the ridge of the Appalachian Mountains in North America. Smaller areas of change are present in various tropical regions. The land cover changes in the model are almost exclusively a conversion of forests to grasslands.
Model results show that, at the global scale, the physical impacts of LCC on temperature and rainfall are less important than large-scale SST anomalies, particularly those due to ENSO. However, in the regions where the land surface has been altered, the impact of LCC can be equally or more important than the SST forcing patterns in determining the seasonal cycle of the surface water and energy balance. Thus, this work provides a context for the impacts of LCC on climate: namely, strong regional-scale impacts that can significantly change globally averaged fields but that rarely propagate beyond the disturbed regions. This suggests that proper representation of land cover conditions is essential in the design of climate model experiments, particularly if results are to be used for regional-scale assessments of climate change impacts.
Abstract
The atmospheric and land components of the Geophysical Fluid Dynamics Laboratory’s (GFDL’s) Climate Model version 2.1 (CM2.1) is used with climatological sea surface temperatures (SSTs) to investigate the relative climatic impacts of historical anthropogenic land cover change (LCC) and realistic SST anomalies. The SST forcing anomalies used are analogous to signals induced by El Niño–Southern Oscillation (ENSO), the North Atlantic Oscillation (NAO), and the background global warming trend. Coherent areas of LCC are represented throughout much of central and eastern Europe, northern India, southeastern China, and on either side of the ridge of the Appalachian Mountains in North America. Smaller areas of change are present in various tropical regions. The land cover changes in the model are almost exclusively a conversion of forests to grasslands.
Model results show that, at the global scale, the physical impacts of LCC on temperature and rainfall are less important than large-scale SST anomalies, particularly those due to ENSO. However, in the regions where the land surface has been altered, the impact of LCC can be equally or more important than the SST forcing patterns in determining the seasonal cycle of the surface water and energy balance. Thus, this work provides a context for the impacts of LCC on climate: namely, strong regional-scale impacts that can significantly change globally averaged fields but that rarely propagate beyond the disturbed regions. This suggests that proper representation of land cover conditions is essential in the design of climate model experiments, particularly if results are to be used for regional-scale assessments of climate change impacts.
Abstract
Tropical cyclones (TCs) moving into the midlatitudes can produce extreme precipitation, as was the case with Hurricane Irene (2011). Despite the high-impact nature of these events, relatively few studies have explored the sensitivity of TC precipitation forecasts to model initial conditions. Here, the physical processes that modulate precipitation forecasts over the Northeast United States during Irene are investigated using an 80-member 0.5° Global Forecasting System (GFS) ensemble. The members that forecast the highest total precipitation over the Catskill Mountains in New York (i.e., wet members) are compared with the members that predicted the least precipitation (i.e., dry members). Results indicate that the amount of rainfall is tied to storm track, with the wetter members forecast to move farther west than the dry members. This variability in storm track appears to be associated with variability in analyzed upper-tropospheric potential vorticity (PV), such that the wetter members feature greater cyclonic PV southwest of Irene when Irene is off the Carolina coast. By contrast, the wetter members of a 3-km Weather Research and Forecasting (WRF) Model ensemble, initialized from the same GFS ensemble forecasts, show little sensitivity to track. Instead, the wetter members are characterized by stronger lower-tropospheric winds perpendicular to the eastern face of the Catskills, allowing maximum upslope forcing and horizontal moisture flux convergence during the period of heaviest rainfall. The drier members, on the other hand, have the greatest quasigeostrophic forcing for ascent, implying that the members’ differences in mesoscale topographic forcing are the dominant influence on rainfall rate.
Abstract
Tropical cyclones (TCs) moving into the midlatitudes can produce extreme precipitation, as was the case with Hurricane Irene (2011). Despite the high-impact nature of these events, relatively few studies have explored the sensitivity of TC precipitation forecasts to model initial conditions. Here, the physical processes that modulate precipitation forecasts over the Northeast United States during Irene are investigated using an 80-member 0.5° Global Forecasting System (GFS) ensemble. The members that forecast the highest total precipitation over the Catskill Mountains in New York (i.e., wet members) are compared with the members that predicted the least precipitation (i.e., dry members). Results indicate that the amount of rainfall is tied to storm track, with the wetter members forecast to move farther west than the dry members. This variability in storm track appears to be associated with variability in analyzed upper-tropospheric potential vorticity (PV), such that the wetter members feature greater cyclonic PV southwest of Irene when Irene is off the Carolina coast. By contrast, the wetter members of a 3-km Weather Research and Forecasting (WRF) Model ensemble, initialized from the same GFS ensemble forecasts, show little sensitivity to track. Instead, the wetter members are characterized by stronger lower-tropospheric winds perpendicular to the eastern face of the Catskills, allowing maximum upslope forcing and horizontal moisture flux convergence during the period of heaviest rainfall. The drier members, on the other hand, have the greatest quasigeostrophic forcing for ascent, implying that the members’ differences in mesoscale topographic forcing are the dominant influence on rainfall rate.
Abstract
This study examines the predictability of seasonal mean Great Plains precipitation using an ensemble of century-long atmospheric general circulation model (AGCM) simulations forced with observed sea surface temperatures (SSTs). The results show that the predictability (intraensemble spread) of the precipitation response to SST forcing varies on interannual and longer time scales. In particular, this study finds that pluvial conditions are more predictable (have less intraensemble spread) than drought conditions. This rather unexpected result is examined in the context of the physical mechanisms that impact precipitation in the Great Plains. These mechanisms include El Niño–Southern Oscillation’s impact on the planetary waves and hence the Pacific storm track (primarily during the cold season), the role of Atlantic SSTs in forcing changes in the Bermuda high and low-level moisture flux into the continent (primarily during the warm season), and soil moisture feedbacks (primarily during the warm season). It is found that the changes in predictability are primarily driven by changes in the strength of the land–atmosphere coupling, such that under dry conditions a given change in soil moisture produces a larger change in evaporation and hence precipitation than the same change in soil moisture would produce under wet soil conditions. The above changes in predictability are associated with a negatively skewed distribution in the seasonal mean precipitation during the warm season—a result that is not inconsistent with the observations.
Abstract
This study examines the predictability of seasonal mean Great Plains precipitation using an ensemble of century-long atmospheric general circulation model (AGCM) simulations forced with observed sea surface temperatures (SSTs). The results show that the predictability (intraensemble spread) of the precipitation response to SST forcing varies on interannual and longer time scales. In particular, this study finds that pluvial conditions are more predictable (have less intraensemble spread) than drought conditions. This rather unexpected result is examined in the context of the physical mechanisms that impact precipitation in the Great Plains. These mechanisms include El Niño–Southern Oscillation’s impact on the planetary waves and hence the Pacific storm track (primarily during the cold season), the role of Atlantic SSTs in forcing changes in the Bermuda high and low-level moisture flux into the continent (primarily during the warm season), and soil moisture feedbacks (primarily during the warm season). It is found that the changes in predictability are primarily driven by changes in the strength of the land–atmosphere coupling, such that under dry conditions a given change in soil moisture produces a larger change in evaporation and hence precipitation than the same change in soil moisture would produce under wet soil conditions. The above changes in predictability are associated with a negatively skewed distribution in the seasonal mean precipitation during the warm season—a result that is not inconsistent with the observations.
Abstract
In this study the authors examine the impact of El Niño–Southern Oscillation (ENSO) on precipitation events over the continental United States using 49 winters (1949/50–1997/98) of daily precipitation observations and NCEP–NCAR reanalyses. The results are compared with those from an ensemble of nine atmospheric general circulation model (AGCM) simulations forced with observed SST for the same time period. Empirical orthogonal functions (EOFs) of the daily precipitation fields together with compositing techniques are used to identify and characterize the weather systems that dominate the winter precipitation variability. The time series of the principal components (PCs) associated with the leading EOFs are analyzed using generalized extreme value (GEV) distributions to quantify the impact of ENSO on the intensity of extreme precipitation events.
The six leading EOFs of the observations are associated with major winter storm systems and account for more than 50% of the daily precipitation variability along the West Coast and over much of the eastern part of the country. Two of the leading EOFs (designated GC for Gulf Coast and EC for East Coast) together represent cyclones that develop in the Gulf of Mexico and occasionally move and/or redevelop along the East Coast producing large amounts of precipitation over much of the southern and eastern United States. Three of the leading EOFs represent storms that hit different sections of the West Coast (designated SW for Southwest coast, WC for the central West Coast, and NW for northwest coast), while another represents storms that affect the Midwest (designated by MW). The winter maxima of several of the leading PCs are significantly impacted by ENSO such that extreme GC, EC, and SW storms that occur on average only once every 20 years (20-yr storms) would occur on average in half that time under sustained El Niño conditions. In contrast, under La Niña conditions, 20-yr GC and EC storms would occur on average about once in 30 years, while there is little impact of La Niña on the intensity of the SW storms. The leading EOFs from the model simulations and their connections to ENSO are for the most part quite realistic. The model, in particular, does very well in simulating the impact of ENSO on the intensity of EC and GC storms. The main model discrepancies are the lack of SW storms and an overall underestimate of the daily precipitation variance.
Abstract
In this study the authors examine the impact of El Niño–Southern Oscillation (ENSO) on precipitation events over the continental United States using 49 winters (1949/50–1997/98) of daily precipitation observations and NCEP–NCAR reanalyses. The results are compared with those from an ensemble of nine atmospheric general circulation model (AGCM) simulations forced with observed SST for the same time period. Empirical orthogonal functions (EOFs) of the daily precipitation fields together with compositing techniques are used to identify and characterize the weather systems that dominate the winter precipitation variability. The time series of the principal components (PCs) associated with the leading EOFs are analyzed using generalized extreme value (GEV) distributions to quantify the impact of ENSO on the intensity of extreme precipitation events.
The six leading EOFs of the observations are associated with major winter storm systems and account for more than 50% of the daily precipitation variability along the West Coast and over much of the eastern part of the country. Two of the leading EOFs (designated GC for Gulf Coast and EC for East Coast) together represent cyclones that develop in the Gulf of Mexico and occasionally move and/or redevelop along the East Coast producing large amounts of precipitation over much of the southern and eastern United States. Three of the leading EOFs represent storms that hit different sections of the West Coast (designated SW for Southwest coast, WC for the central West Coast, and NW for northwest coast), while another represents storms that affect the Midwest (designated by MW). The winter maxima of several of the leading PCs are significantly impacted by ENSO such that extreme GC, EC, and SW storms that occur on average only once every 20 years (20-yr storms) would occur on average in half that time under sustained El Niño conditions. In contrast, under La Niña conditions, 20-yr GC and EC storms would occur on average about once in 30 years, while there is little impact of La Niña on the intensity of the SW storms. The leading EOFs from the model simulations and their connections to ENSO are for the most part quite realistic. The model, in particular, does very well in simulating the impact of ENSO on the intensity of EC and GC storms. The main model discrepancies are the lack of SW storms and an overall underestimate of the daily precipitation variance.
Abstract
The predictability of the autumn, boreal winter, and spring seasons with foreknowledge of sea surface temperatures (SSTs) is studied using ensembles of seasonal simulations of three general circulation models (GCMs): the Center for Ocean–Land–Atmosphere Studies (COLA) GCM, the National Aeronautics and Space Administration Seasonal to Interannual Prediction Project (NSIPP) GCM, and the National Centers for Environmental Prediction (NCEP) GCM. Warm-minus-cold composites of the ensemble mean and observed tropical Pacific precipitation, averaged for the three warmest El Niño and three coldest La Niña winters, show large positive anomalies near the date line that extend eastward to the South American coast. The same is true for composites of the spring following the event. In the composites of the autumn preceding the event, the precipitation is weaker and shifted off the equator in the far eastern Pacific, where equatorial SSTs are too low to support convection. The corresponding boreal winter 200-hPa height composites show strong signals in the Tropics and midlatitudes of both hemispheres. The subsequent spring composites are similar, but weaker in the northern extratropics. In the preceding autumn composites, the overall height signal is quite weak, except in the southern Pacific.
The model dependence of the signal (variance of ensemble means) and noise (variance about the ensemble means) of the seasonal mean 200-hPa height is small, a result that holds for all three seasons and is in contrast to earlier studies. The signal-to-noise ratio is significantly greater than unity in the Tropics (all seasons), the northern Pacific and continental North America subtropics (boreal winter and spring), and the southern Pacific subtropics (boreal autumn).
Rotated empirical orthogonal function analysis of the tropical Pacific SST recovers El Niño–like dominant patterns in boreal winter and spring, but emphasizes two SST patterns in autumn, one with largest SST in the far eastern tropical Pacific and one with a maximum nearer the date line. Two methods are used to assess the precipitation and height field responses to these patterns: linear regression of the ensemble means on the principal component (PC) time series of SST and identification of patterns that optimize the signal-to-noise ratio. The two methods yield remarkably similar results.
The optimal height patterns for boreal winter and spring are similar, although the spring response over the northern extratropics is somewhat weaker, and some subtle changes in phase are found in all three GCMs. The associated optimal time series have serial correlations with the leading PC of SST that exceed 0.9 for all GCMs for winter and spring. For autumn the time series of the leading two optimal patterns each has a serial correlation with the corresponding PC of SST that exceeds 0.7 for the COLA and NSIPP GCMs. The autumn 200-hPa-height leading optimal pattern (response to eastern Pacific SST) is quite weak, representing nearly uniform tropical warming. The second optimal pattern (response to central Pacific SST) shows a robust wave train in the southern Pacific, with a consistent belt of low height over northern midlatitudes.
Abstract
The predictability of the autumn, boreal winter, and spring seasons with foreknowledge of sea surface temperatures (SSTs) is studied using ensembles of seasonal simulations of three general circulation models (GCMs): the Center for Ocean–Land–Atmosphere Studies (COLA) GCM, the National Aeronautics and Space Administration Seasonal to Interannual Prediction Project (NSIPP) GCM, and the National Centers for Environmental Prediction (NCEP) GCM. Warm-minus-cold composites of the ensemble mean and observed tropical Pacific precipitation, averaged for the three warmest El Niño and three coldest La Niña winters, show large positive anomalies near the date line that extend eastward to the South American coast. The same is true for composites of the spring following the event. In the composites of the autumn preceding the event, the precipitation is weaker and shifted off the equator in the far eastern Pacific, where equatorial SSTs are too low to support convection. The corresponding boreal winter 200-hPa height composites show strong signals in the Tropics and midlatitudes of both hemispheres. The subsequent spring composites are similar, but weaker in the northern extratropics. In the preceding autumn composites, the overall height signal is quite weak, except in the southern Pacific.
The model dependence of the signal (variance of ensemble means) and noise (variance about the ensemble means) of the seasonal mean 200-hPa height is small, a result that holds for all three seasons and is in contrast to earlier studies. The signal-to-noise ratio is significantly greater than unity in the Tropics (all seasons), the northern Pacific and continental North America subtropics (boreal winter and spring), and the southern Pacific subtropics (boreal autumn).
Rotated empirical orthogonal function analysis of the tropical Pacific SST recovers El Niño–like dominant patterns in boreal winter and spring, but emphasizes two SST patterns in autumn, one with largest SST in the far eastern tropical Pacific and one with a maximum nearer the date line. Two methods are used to assess the precipitation and height field responses to these patterns: linear regression of the ensemble means on the principal component (PC) time series of SST and identification of patterns that optimize the signal-to-noise ratio. The two methods yield remarkably similar results.
The optimal height patterns for boreal winter and spring are similar, although the spring response over the northern extratropics is somewhat weaker, and some subtle changes in phase are found in all three GCMs. The associated optimal time series have serial correlations with the leading PC of SST that exceed 0.9 for all GCMs for winter and spring. For autumn the time series of the leading two optimal patterns each has a serial correlation with the corresponding PC of SST that exceeds 0.7 for the COLA and NSIPP GCMs. The autumn 200-hPa-height leading optimal pattern (response to eastern Pacific SST) is quite weak, representing nearly uniform tropical warming. The second optimal pattern (response to central Pacific SST) shows a robust wave train in the southern Pacific, with a consistent belt of low height over northern midlatitudes.
Abstract
The observed climate trends over the United States during 1950–2000 exhibit distinct seasonality and regionality. The surface air temperature exhibits a warming trend during winter, spring, and early summer and a modest countrywide cooling trend in late summer and fall, with the strongest warming occurring over the northern United States in spring. Precipitation trends are positive in all seasons, with the largest trend occurring over the central and southern United States in fall. This study investigates the causes of the seasonality and regionality of those trends, with a focus on the cooling and wetting trends in the central United States during late summer and fall. In particular, the authors examine the link between the seasonality and regionality of the climate trends over the United States and the leading patterns of sea surface temperature (SST) variability, including a global warming (GW) pattern and a Pacific decadal variability (PDV) pattern.
A series of idealized atmospheric general circulation model (AGCM) experiments were performed forced by SST trends associated with these leading SST patterns, as well as the residual trend pattern (obtained by removing the GW and PDV contributions). The results show that the observed seasonal and spatial variations of the climate trends over the United States are to a large extent explained by changes in SST. Among the leading patterns of SST variability, the PDV pattern plays a prominent role in producing both the seasonality and regionality of the climate trends over the United States. In particular, it is the main contributor to the apparent cooling and wetting trends over the central United States. The residual SST trend, a manifestation of phase changes of the Atlantic multidecadal SST variation during 1950–2000, also exerts influences that show strong seasonality with important contributions to the central U.S. temperature and precipitation during the summer and fall seasons. In contrast, the response over the United States to the GW SST pattern is an overall warming with little seasonality or regional variation. These results highlight the important contributions of decadal and multidecadal variability in the Pacific and Atlantic in explaining the observed seasonality and regionality of the climate trends over the United States during the period of 1950–2000.
Abstract
The observed climate trends over the United States during 1950–2000 exhibit distinct seasonality and regionality. The surface air temperature exhibits a warming trend during winter, spring, and early summer and a modest countrywide cooling trend in late summer and fall, with the strongest warming occurring over the northern United States in spring. Precipitation trends are positive in all seasons, with the largest trend occurring over the central and southern United States in fall. This study investigates the causes of the seasonality and regionality of those trends, with a focus on the cooling and wetting trends in the central United States during late summer and fall. In particular, the authors examine the link between the seasonality and regionality of the climate trends over the United States and the leading patterns of sea surface temperature (SST) variability, including a global warming (GW) pattern and a Pacific decadal variability (PDV) pattern.
A series of idealized atmospheric general circulation model (AGCM) experiments were performed forced by SST trends associated with these leading SST patterns, as well as the residual trend pattern (obtained by removing the GW and PDV contributions). The results show that the observed seasonal and spatial variations of the climate trends over the United States are to a large extent explained by changes in SST. Among the leading patterns of SST variability, the PDV pattern plays a prominent role in producing both the seasonality and regionality of the climate trends over the United States. In particular, it is the main contributor to the apparent cooling and wetting trends over the central United States. The residual SST trend, a manifestation of phase changes of the Atlantic multidecadal SST variation during 1950–2000, also exerts influences that show strong seasonality with important contributions to the central U.S. temperature and precipitation during the summer and fall seasons. In contrast, the response over the United States to the GW SST pattern is an overall warming with little seasonality or regional variation. These results highlight the important contributions of decadal and multidecadal variability in the Pacific and Atlantic in explaining the observed seasonality and regionality of the climate trends over the United States during the period of 1950–2000.
