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- Author or Editor: Xin-Zhong Liang x
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Abstract
Most climate models in phase 6 of the Coupled Model Intercomparison Project (CMIP6) still suffer pronounced warm and dry summer biases in the central United States (CUS), even in high-resolution simulations. We found that the cloud base definition in the cumulus parameterization was the dominant factor determining the spread of the biases among models and those defining cloud base at the lifting condensation level (LCL) performed the best. To identify the underlying mechanisms, we developed a physically based analytical bias model (ABM) to capture the key feedback processes of land–atmosphere coupling. The ABM has significant explanatory power, capturing 80% variance of temperature and precipitation biases among all models. Our ABM analysis via counterfactual experiments indicated that the biases are attributed mostly by surface downwelling longwave radiation errors and second by surface net shortwave radiation errors, with the former 2–5 times larger. The effective radiative forcing from these two errors as weighted by their relative contributions induces runaway temperature and precipitation feedbacks, which collaborate to cause CUS summer warm and dry biases. The LCL cumulus reduces the biases through two key mechanisms: it produces more clouds and less precipitable water, which reduce radiative energy input for both surface heating and evapotranspiration to cause a cooler and wetter soil; it produces more rainfall and wetter soil conditions, which suppress the positive evapotranspiration–precipitation feedback to damp the warm and dry bias coupling. Most models using non-LCL schemes underestimate both precipitation and cloud amounts, which amplify the positive feedback to cause significant biases.
Abstract
Most climate models in phase 6 of the Coupled Model Intercomparison Project (CMIP6) still suffer pronounced warm and dry summer biases in the central United States (CUS), even in high-resolution simulations. We found that the cloud base definition in the cumulus parameterization was the dominant factor determining the spread of the biases among models and those defining cloud base at the lifting condensation level (LCL) performed the best. To identify the underlying mechanisms, we developed a physically based analytical bias model (ABM) to capture the key feedback processes of land–atmosphere coupling. The ABM has significant explanatory power, capturing 80% variance of temperature and precipitation biases among all models. Our ABM analysis via counterfactual experiments indicated that the biases are attributed mostly by surface downwelling longwave radiation errors and second by surface net shortwave radiation errors, with the former 2–5 times larger. The effective radiative forcing from these two errors as weighted by their relative contributions induces runaway temperature and precipitation feedbacks, which collaborate to cause CUS summer warm and dry biases. The LCL cumulus reduces the biases through two key mechanisms: it produces more clouds and less precipitable water, which reduce radiative energy input for both surface heating and evapotranspiration to cause a cooler and wetter soil; it produces more rainfall and wetter soil conditions, which suppress the positive evapotranspiration–precipitation feedback to damp the warm and dry bias coupling. Most models using non-LCL schemes underestimate both precipitation and cloud amounts, which amplify the positive feedback to cause significant biases.
Abstract
Most climate models still suffer large warm and dry summer biases in the central United States (CUS). As a solution, we improved cumulus parameterization to represent 1) the lifting effect of small-scale rising motions associated with Great Plains low-level jets and midtropospheric perturbations by defining the cloud base at the level of condensation, 2) the constraint of the cumulus entrainment rate depending on the boundary layer depth, and 3) the temperature-dependent cloud-to-rainwater conversion rate. These improvements acted to (i) trigger mesoscale convective systems in unfavorable environmental conditions to enhance total rainfall amount, (ii) lower cloud base and increase cloud depth to increase low-level clouds and reduce surface shortwave radiation, (iii) suppress penetrative cumuli from shallow boundary layers to remedy the overestimation of precipitation frequency, and (iv) increase water detrainment to form sufficient cirrus clouds and balanced outgoing longwave radiation. Much of these effects were nonlocal and nonlinear, where more frequent but weaker convective rainfall led to stronger (and sometimes more frequent) large-scale precipitation remotely. Together, they produced consistently heavier precipitation and colder temperature with a realistic atmospheric energy balance, essentially eliminating the CUS warm and dry biases through robust physical mechanisms.
Abstract
Most climate models still suffer large warm and dry summer biases in the central United States (CUS). As a solution, we improved cumulus parameterization to represent 1) the lifting effect of small-scale rising motions associated with Great Plains low-level jets and midtropospheric perturbations by defining the cloud base at the level of condensation, 2) the constraint of the cumulus entrainment rate depending on the boundary layer depth, and 3) the temperature-dependent cloud-to-rainwater conversion rate. These improvements acted to (i) trigger mesoscale convective systems in unfavorable environmental conditions to enhance total rainfall amount, (ii) lower cloud base and increase cloud depth to increase low-level clouds and reduce surface shortwave radiation, (iii) suppress penetrative cumuli from shallow boundary layers to remedy the overestimation of precipitation frequency, and (iv) increase water detrainment to form sufficient cirrus clouds and balanced outgoing longwave radiation. Much of these effects were nonlocal and nonlinear, where more frequent but weaker convective rainfall led to stronger (and sometimes more frequent) large-scale precipitation remotely. Together, they produced consistently heavier precipitation and colder temperature with a realistic atmospheric energy balance, essentially eliminating the CUS warm and dry biases through robust physical mechanisms.
Abstract
A diagnostic analysis of relationships between central U.S. climate characteristics and various flow and scalar fields was used to evaluate nine global coupled ocean–atmosphere general circulation models (CGCMs) participating in the Coupled Model Intercomparison Project (CMIP). To facilitate identification of physical mechanisms causing biases, data from 21 models participating in the Atmospheric Model Intercomparison Project (AMIP) were also used for certain key analyses.
Most models reproduce basic features of the circulation, temperature, and precipitation patterns in the central United States, although no model exhibits small differences from the observationally based data for all characteristics in all seasons. Model ensemble means generally produce better agreement with the observationally based data than any single model. A fall precipitation deficiency, found in all AMIP and CMIP models except the third-generation Hadley Centre CGCM (HadCM3), appears to be related in part to slight biases in the flow on the western flank of the Atlantic subtropical ridge. In the model mean, the ridge at 850 hPa is displaced slightly to the north and to the west, resulting in weaker southerly flow into the central United States.
The CMIP doubled-CO2 transient runs show warming (1°–5°C) for all models and seasons and variable precipitation changes over the central United States. Temperature (precipitation) changes are larger (mostly less) than the variations that are observed in the twentieth century and the model variations in the control simulations.
Abstract
A diagnostic analysis of relationships between central U.S. climate characteristics and various flow and scalar fields was used to evaluate nine global coupled ocean–atmosphere general circulation models (CGCMs) participating in the Coupled Model Intercomparison Project (CMIP). To facilitate identification of physical mechanisms causing biases, data from 21 models participating in the Atmospheric Model Intercomparison Project (AMIP) were also used for certain key analyses.
Most models reproduce basic features of the circulation, temperature, and precipitation patterns in the central United States, although no model exhibits small differences from the observationally based data for all characteristics in all seasons. Model ensemble means generally produce better agreement with the observationally based data than any single model. A fall precipitation deficiency, found in all AMIP and CMIP models except the third-generation Hadley Centre CGCM (HadCM3), appears to be related in part to slight biases in the flow on the western flank of the Atlantic subtropical ridge. In the model mean, the ridge at 850 hPa is displaced slightly to the north and to the west, resulting in weaker southerly flow into the central United States.
The CMIP doubled-CO2 transient runs show warming (1°–5°C) for all models and seasons and variable precipitation changes over the central United States. Temperature (precipitation) changes are larger (mostly less) than the variations that are observed in the twentieth century and the model variations in the control simulations.
Abstract
Observations reveal that, in summer, westward extension of the Bermuda high enhances the Great Plains low-level jet (LLJ) that transports more moisture northward, causing precipitation increases in the Midwest and decreases in the Gulf States. Meanwhile, more warm air advection from the Gulf of Mexico to the southern Great Plains and stronger clear-sky radiative heating under high pressures over the Southeast result in warmer surface temperatures across the Gulf states. The enhanced LLJ transport of cleaner marine air from the Gulf reduces surface ozone across the southern Great Plains–Midwest. In contrast, larger transport of more polluted air from the Midwest to New England and more frequent air stagnation under high pressures in the Southeast increase ozone over most of the eastern coastal states. This Bermuda high–induced ozone change reversal between the southern Great Plains–Midwest and eastern coastal states, with a magnitude of 6 and 13.5 ppb, respectively, in summer-mean maximum daily 8-h average, exhibits strong decadal variations that should be considered in the U.S. air quality dynamic management.
The observed Bermuda high signatures over the Gulf states can be well captured by regional climate and air quality models. Notable model deficiencies exist over the northern Great Plains–Midwest that are more remote to the Bermuda high and LLJ control. The regional models largely reduce these deficiencies from general circulation models (GCMs). Only 7 out of 51 GCMs can represent all key regional signatures of the Bermuda high, while none can simulate its strong association with planetary sea surface temperature anomalies. The result indicates a great challenge for GCMs to predict Bermuda high variability and change.
Abstract
Observations reveal that, in summer, westward extension of the Bermuda high enhances the Great Plains low-level jet (LLJ) that transports more moisture northward, causing precipitation increases in the Midwest and decreases in the Gulf States. Meanwhile, more warm air advection from the Gulf of Mexico to the southern Great Plains and stronger clear-sky radiative heating under high pressures over the Southeast result in warmer surface temperatures across the Gulf states. The enhanced LLJ transport of cleaner marine air from the Gulf reduces surface ozone across the southern Great Plains–Midwest. In contrast, larger transport of more polluted air from the Midwest to New England and more frequent air stagnation under high pressures in the Southeast increase ozone over most of the eastern coastal states. This Bermuda high–induced ozone change reversal between the southern Great Plains–Midwest and eastern coastal states, with a magnitude of 6 and 13.5 ppb, respectively, in summer-mean maximum daily 8-h average, exhibits strong decadal variations that should be considered in the U.S. air quality dynamic management.
The observed Bermuda high signatures over the Gulf states can be well captured by regional climate and air quality models. Notable model deficiencies exist over the northern Great Plains–Midwest that are more remote to the Bermuda high and LLJ control. The regional models largely reduce these deficiencies from general circulation models (GCMs). Only 7 out of 51 GCMs can represent all key regional signatures of the Bermuda high, while none can simulate its strong association with planetary sea surface temperature anomalies. The result indicates a great challenge for GCMs to predict Bermuda high variability and change.
Abstract
An observational climatology of the planetary boundary layer height (PBLH) diurnal cycle, specific to surface characteristics, is derived from 58 286 fine-resolution soundings collected in 14 major field campaigns around the world. An objective algorithm determining PBLH from sounding profiles is first developed and then verified by available lidar and sodar retrievals. The algorithm is robust and produces realistic PBLH as validated by visual examination of several thousand additional soundings. The resulting PBLH from all existing data is then subject to various statistical analyses. It is demonstrated that PBLH occurrence frequencies under stable, neutral, and unstable regimes follow a narrow, intermediate, and wide Gamma distribution, respectively, over both land and oceans. Over ice all exhibit a narrow distribution. The climatological PBLH diurnal cycle is strong over land and oceans, with a distinct peak at 1500 and 1200 LT, whereas the cycle is weak over ice. Relative to midlatitude land, the PBLH variability over tropical oceans is larger during the morning and at night but much smaller in the afternoon. This study provides a unique observational database for critical model evaluation on the PBLH diurnal cycle and its temporal/spatial variability.
Abstract
An observational climatology of the planetary boundary layer height (PBLH) diurnal cycle, specific to surface characteristics, is derived from 58 286 fine-resolution soundings collected in 14 major field campaigns around the world. An objective algorithm determining PBLH from sounding profiles is first developed and then verified by available lidar and sodar retrievals. The algorithm is robust and produces realistic PBLH as validated by visual examination of several thousand additional soundings. The resulting PBLH from all existing data is then subject to various statistical analyses. It is demonstrated that PBLH occurrence frequencies under stable, neutral, and unstable regimes follow a narrow, intermediate, and wide Gamma distribution, respectively, over both land and oceans. Over ice all exhibit a narrow distribution. The climatological PBLH diurnal cycle is strong over land and oceans, with a distinct peak at 1500 and 1200 LT, whereas the cycle is weak over ice. Relative to midlatitude land, the PBLH variability over tropical oceans is larger during the morning and at night but much smaller in the afternoon. This study provides a unique observational database for critical model evaluation on the PBLH diurnal cycle and its temporal/spatial variability.
Abstract
The fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5)-based regional climate model (CMM5) capability in simulating the interannual variations of U.S. precipitation and surface air temperature during 1982–2002 is evaluated with a continuous baseline integration driven by the NCEP–Department of Energy (DOE) Second Atmospheric Model Intercomparison Project Reanalysis (R-2). It is demonstrated that the CMM5 has a pronounced downscaling skill for precipitation and temperature interannual variations. The EOF and correlation analyses illustrate that, for both quantities, the CMM5 captures the spatial pattern, temporal evolution, and circulation teleconnections much better than the R-2. In particular, the CMM5 more realistically simulates the precipitation pattern centered in the Northwest, where the representation of the orographic enhancement by the forced uplifting during winter (rainy season) is greatly improved over the R-2.
The downscaling skill, however, is sensitive to the cumulus parameterization. This sensitivity is studied by comparing the baseline with a branch summer integration replacing the Grell with the Kain–Fritsch cumulus scheme in the CMM5. The dominant EOF mode of the U.S. summer precipitation interannual variation, identified with the out-of-phase relationship between the Midwest and Southeast in observations, is reproduced more accurately by the Grell than the Kain–Fritsch scheme, which largely underestimates the variation in the Midwest. This pattern is associated with east–west movement of the Great Plains low-level jet (LLJ): a more western position corresponds to a stronger southerly flow bringing more moisture and heavier rainfall in the Midwest and less in the Southeast. The second EOF pattern, which describes the consistent variation over the southern part of the Midwest and the South in observations, is captured better by the Kain–Fritsch scheme than the Grell, whose pattern systematically shifts southward.
Abstract
The fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5)-based regional climate model (CMM5) capability in simulating the interannual variations of U.S. precipitation and surface air temperature during 1982–2002 is evaluated with a continuous baseline integration driven by the NCEP–Department of Energy (DOE) Second Atmospheric Model Intercomparison Project Reanalysis (R-2). It is demonstrated that the CMM5 has a pronounced downscaling skill for precipitation and temperature interannual variations. The EOF and correlation analyses illustrate that, for both quantities, the CMM5 captures the spatial pattern, temporal evolution, and circulation teleconnections much better than the R-2. In particular, the CMM5 more realistically simulates the precipitation pattern centered in the Northwest, where the representation of the orographic enhancement by the forced uplifting during winter (rainy season) is greatly improved over the R-2.
The downscaling skill, however, is sensitive to the cumulus parameterization. This sensitivity is studied by comparing the baseline with a branch summer integration replacing the Grell with the Kain–Fritsch cumulus scheme in the CMM5. The dominant EOF mode of the U.S. summer precipitation interannual variation, identified with the out-of-phase relationship between the Midwest and Southeast in observations, is reproduced more accurately by the Grell than the Kain–Fritsch scheme, which largely underestimates the variation in the Midwest. This pattern is associated with east–west movement of the Great Plains low-level jet (LLJ): a more western position corresponds to a stronger southerly flow bringing more moisture and heavier rainfall in the Midwest and less in the Southeast. The second EOF pattern, which describes the consistent variation over the southern part of the Midwest and the South in observations, is captured better by the Kain–Fritsch scheme than the Grell, whose pattern systematically shifts southward.
Abstract
Computations of precipitation recycling using analytical models are generally performed under the assumption of negligible change in moisture storage in the atmospheric column. Because the moisture storage term is nonnegligible at smaller time scales, most recycling studies using analytical models are done at monthly or longer time scales. A dynamic precipitation recycling model, which incorporates the change in moisture storage, is developed. It is derived formally from the conservation of mass equation and is presented in a simple and computationally efficient form. This model allows for recycling analysis at a range of temporal scales, from daily to monthly and longer. In comparison to the traditional models that do not include the storage term, the new model presents almost identical spatial and temporal variability, but predicts recycling ratios that are 12%–33% larger at a monthly level.
The dynamic model is used to study the variability of monthly precipitation recycling over the conterminous United States using Reanalysis-II data from 1979 to 2000. On average, the southeastern and southwestern parts of the country exhibit high summer recycling ratios, contrasting with the low values in the northeastern and northwestern United States. The Colorado region also presents high recycling ratios. Dominant modes of spatiotemporal variability in recycling are identified using EOF analysis. The first mode captures strong recycling ratios over the western United States during the summers of 1986, 1992, and 1998. The second mode captures anomalous high recycling ratios during 1988 and 1989 over the central part of the country, and anomalous low ratios during 1980 and 1993.
Abstract
Computations of precipitation recycling using analytical models are generally performed under the assumption of negligible change in moisture storage in the atmospheric column. Because the moisture storage term is nonnegligible at smaller time scales, most recycling studies using analytical models are done at monthly or longer time scales. A dynamic precipitation recycling model, which incorporates the change in moisture storage, is developed. It is derived formally from the conservation of mass equation and is presented in a simple and computationally efficient form. This model allows for recycling analysis at a range of temporal scales, from daily to monthly and longer. In comparison to the traditional models that do not include the storage term, the new model presents almost identical spatial and temporal variability, but predicts recycling ratios that are 12%–33% larger at a monthly level.
The dynamic model is used to study the variability of monthly precipitation recycling over the conterminous United States using Reanalysis-II data from 1979 to 2000. On average, the southeastern and southwestern parts of the country exhibit high summer recycling ratios, contrasting with the low values in the northeastern and northwestern United States. The Colorado region also presents high recycling ratios. Dominant modes of spatiotemporal variability in recycling are identified using EOF analysis. The first mode captures strong recycling ratios over the western United States during the summers of 1986, 1992, and 1998. The second mode captures anomalous high recycling ratios during 1988 and 1989 over the central part of the country, and anomalous low ratios during 1980 and 1993.
Abstract
The characteristics of deep-layer terrestrial memory are explored using observed soil moisture data and simulated soil temperature from the Illinois Climate Network stations. Both soil moisture and soil temperature are characterized by exponential decay in amplitude, linear lag in phase, and increasing persistence with depth. Using spectral analysis, four dominant low-frequency modes are identified in the soil moisture variability. These signals have periods of about 12, 17, 34, and 60 months, which correspond to annual cycle, (4/3) ENSO, quasi-biennial (QB) ENSO, and quasi-quadrennial (QQ) ENSO signals, respectively. For deep layers, the interannual modes are dominant over the annual cycle, and vice versa for the near-surface layer. There are inherently two mechanisms by which deep-layer moisture impacts the surface fluxes. First, its temporal variability sets the lower boundary condition for the transfer of moisture and heat fluxes from the surface. Second, this temporal variability influences the uptake of moisture by plant roots, resulting in the variability of the transpiration and, therefore, the entire energy balance. Initial results suggest that this second mechanism may be more predominant.
Abstract
The characteristics of deep-layer terrestrial memory are explored using observed soil moisture data and simulated soil temperature from the Illinois Climate Network stations. Both soil moisture and soil temperature are characterized by exponential decay in amplitude, linear lag in phase, and increasing persistence with depth. Using spectral analysis, four dominant low-frequency modes are identified in the soil moisture variability. These signals have periods of about 12, 17, 34, and 60 months, which correspond to annual cycle, (4/3) ENSO, quasi-biennial (QB) ENSO, and quasi-quadrennial (QQ) ENSO signals, respectively. For deep layers, the interannual modes are dominant over the annual cycle, and vice versa for the near-surface layer. There are inherently two mechanisms by which deep-layer moisture impacts the surface fluxes. First, its temporal variability sets the lower boundary condition for the transfer of moisture and heat fluxes from the surface. Second, this temporal variability influences the uptake of moisture by plant roots, resulting in the variability of the transpiration and, therefore, the entire energy balance. Initial results suggest that this second mechanism may be more predominant.
Abstract
Yearly variations in the observed initial and final dates of heavy, persistent monsoon rainband precipitation across China are quantified. The development of a semiobjective analysis that identifies these values also makes it possible to calculate annual rainband duration and total rainfall. Relationships between total rainband precipitation and the Eurasian circulation are then determined. This research is designed such that observed rainband characteristics can be used in future investigations to evaluate GCM simulations.
Normalized daily precipitation time series are analyzed between 1951 and 1990 for 85 observation stations to develop criteria that describe general rainband characteristics throughout China. Rainfall is defined to be “heavy” if the daily value at a given location is greater than 1.5% of the annual mean total. Heavy precipitation is then shown to be “persistent” and is thus identified with the rainband when the 1.5% threshold is exceeded at least 6 times in a 25-day period. Finally, rainband initial (final) dates are defined to immediately follow (precede) a minimum period of 5 consecutive days with no measurable precipitation. A semiobjective analysis based on the above definitions and rainband climatology is then applied to the time series to determine annual initial and final dates.
Analysis application produces results that closely correspond to the systematic pattern observed across China, where the rainband arrives in the south during May, advances to the Yangtze River valley in June, and then to the north in July. Rainband duration (i.e., final − initial + 1) is approximately 30–40 days while total rainfall decreases from south to north. A significant positive correlation is found between total rainfall and duration interannual variability, where increased rainband precipitation corresponds to initial (final) dates that are anomalously early (late). No clear trends are identified except over north China, where both duration and total rainfall decrease substantially after 1967.
The Eurasian sea level pressure and 500-hPa height fields are then correlated with total rainfall over south China, the Yangtze River valley, and north China to identify statistically significant relationships. Results indicate that precipitation amount is influenced by the interaction of several circulation features. Total rainfall increases over south China when the surface Siberian high ridges to the south and is overrun by warm moist air aloft. Yangtze River valley precipitation intensifies when westward expansion of the subtropical high along with strengthening of the Siberian high and monsoon low cause moisture advection, upward motion, and the thermal gradient along the Mei-Yu front to increase. North China total rainfall increases in response to intense heating over the landmass, westward ridging of the subtropical high, and greater moisture transport over the region.
Abstract
Yearly variations in the observed initial and final dates of heavy, persistent monsoon rainband precipitation across China are quantified. The development of a semiobjective analysis that identifies these values also makes it possible to calculate annual rainband duration and total rainfall. Relationships between total rainband precipitation and the Eurasian circulation are then determined. This research is designed such that observed rainband characteristics can be used in future investigations to evaluate GCM simulations.
Normalized daily precipitation time series are analyzed between 1951 and 1990 for 85 observation stations to develop criteria that describe general rainband characteristics throughout China. Rainfall is defined to be “heavy” if the daily value at a given location is greater than 1.5% of the annual mean total. Heavy precipitation is then shown to be “persistent” and is thus identified with the rainband when the 1.5% threshold is exceeded at least 6 times in a 25-day period. Finally, rainband initial (final) dates are defined to immediately follow (precede) a minimum period of 5 consecutive days with no measurable precipitation. A semiobjective analysis based on the above definitions and rainband climatology is then applied to the time series to determine annual initial and final dates.
Analysis application produces results that closely correspond to the systematic pattern observed across China, where the rainband arrives in the south during May, advances to the Yangtze River valley in June, and then to the north in July. Rainband duration (i.e., final − initial + 1) is approximately 30–40 days while total rainfall decreases from south to north. A significant positive correlation is found between total rainfall and duration interannual variability, where increased rainband precipitation corresponds to initial (final) dates that are anomalously early (late). No clear trends are identified except over north China, where both duration and total rainfall decrease substantially after 1967.
The Eurasian sea level pressure and 500-hPa height fields are then correlated with total rainfall over south China, the Yangtze River valley, and north China to identify statistically significant relationships. Results indicate that precipitation amount is influenced by the interaction of several circulation features. Total rainfall increases over south China when the surface Siberian high ridges to the south and is overrun by warm moist air aloft. Yangtze River valley precipitation intensifies when westward expansion of the subtropical high along with strengthening of the Siberian high and monsoon low cause moisture advection, upward motion, and the thermal gradient along the Mei-Yu front to increase. North China total rainfall increases in response to intense heating over the landmass, westward ridging of the subtropical high, and greater moisture transport over the region.
Abstract
Observed and general circulation climate model (GCM) simulated interannual teleconnection patterns in the Northern Hemisphere are compared on a monthly basis. The study was based on 1946–1991 observations and two separate 100-year simulations corresponding to the present climate and a greenhouse warming climate. The teleconnection patterns are characterized by action centers and composite extreme anomaly (CEA) distributions. The definition and comparison of observed and simulated patterns include examination of time persistence, spatial coherence as well as consistent signatures between 500-mb height, sea level pressure, and surface air temperature.
For the present climate simulation, the GCM reproduces observed spatial and temporal variations of the action centers of four principal teleconnection patterns: the North Atlantic oscillation, the North Pacific oscillation, the Pacific/North American pattern, and the Eurasian pattern. Substantial model biases exist in the magnitude, regional structure as well as monthly transition of anomalies. The CEA regional characteristics are better simulated over land than over the oceans. For example, the model most accurately simulates the Eurasian pattern, which has its dominant action centers over Eurasia. In addition, all three climate variables exhibit substantial anomalies for each land-based action center. In contrast, over the oceans, the model systematically underestimates sea level pressure and 500-mb height CEAs, while it produces small surface temperature responses. It is suggested that atmospheric dynamics associated with flow instability is likely to be the dominant mechanism that generates these teleconnections, while the lack of interactive ocean dynamics may be responsible for small responses over the oceans.
In the greenhouse warming climate, the GCM continues to simulate the four interannual teleconnection patterns. Systematic changes, however, are found for the Pacific/North American and Eurasian patterns in winter, where the action centers shift to the east and the CEAs weaken over land. These results must be considered to be exploratory because of the use of a mixed layer ocean that does not include oceanic dynamics.
Abstract
Observed and general circulation climate model (GCM) simulated interannual teleconnection patterns in the Northern Hemisphere are compared on a monthly basis. The study was based on 1946–1991 observations and two separate 100-year simulations corresponding to the present climate and a greenhouse warming climate. The teleconnection patterns are characterized by action centers and composite extreme anomaly (CEA) distributions. The definition and comparison of observed and simulated patterns include examination of time persistence, spatial coherence as well as consistent signatures between 500-mb height, sea level pressure, and surface air temperature.
For the present climate simulation, the GCM reproduces observed spatial and temporal variations of the action centers of four principal teleconnection patterns: the North Atlantic oscillation, the North Pacific oscillation, the Pacific/North American pattern, and the Eurasian pattern. Substantial model biases exist in the magnitude, regional structure as well as monthly transition of anomalies. The CEA regional characteristics are better simulated over land than over the oceans. For example, the model most accurately simulates the Eurasian pattern, which has its dominant action centers over Eurasia. In addition, all three climate variables exhibit substantial anomalies for each land-based action center. In contrast, over the oceans, the model systematically underestimates sea level pressure and 500-mb height CEAs, while it produces small surface temperature responses. It is suggested that atmospheric dynamics associated with flow instability is likely to be the dominant mechanism that generates these teleconnections, while the lack of interactive ocean dynamics may be responsible for small responses over the oceans.
In the greenhouse warming climate, the GCM continues to simulate the four interannual teleconnection patterns. Systematic changes, however, are found for the Pacific/North American and Eurasian patterns in winter, where the action centers shift to the east and the CEAs weaken over land. These results must be considered to be exploratory because of the use of a mixed layer ocean that does not include oceanic dynamics.