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- Author or Editor: Jin-Ho Yoon x
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Abstract
Using the NCEP–NCAR reanalysis data for the past four decades (1954–97), the interdecadal variation of the North Pacific winter blocking is examined. A noticeable impact on this blocking activity exerted by the Pacific decadal oscillation (PDO: deepening of the Aleutian low and amplification of the Pacific Northwest ridge) was observed. This effect included an interdecadal increasing trend of blocking days [6.5 days (40 yr)−1] and an eastward shift of blocking activity [8.7° longitude (40 yr)−1]. The possible PDO effect on the North Pacific blocking is inferred dynamically from a streamfunction budget analysis.
Abstract
Using the NCEP–NCAR reanalysis data for the past four decades (1954–97), the interdecadal variation of the North Pacific winter blocking is examined. A noticeable impact on this blocking activity exerted by the Pacific decadal oscillation (PDO: deepening of the Aleutian low and amplification of the Pacific Northwest ridge) was observed. This effect included an interdecadal increasing trend of blocking days [6.5 days (40 yr)−1] and an eastward shift of blocking activity [8.7° longitude (40 yr)−1]. The possible PDO effect on the North Pacific blocking is inferred dynamically from a streamfunction budget analysis.
Abstract
It is not unreasonable to expect that boreal forests that exist along 60°N in the Eurasian and North American continents were created and are maintained by warm seasonal rainfall. As revealed from satellite observations and various precipitation sources, zonally elongated rainbelts appear along these forests. Previous studies show that a relationship may exist between the frontal zone along the Arctic seaboard and regional patterns of high-latitude precipitation. It was observed by this study that baroclinic zones associated with strong Arctic westerlies coincide with minor storm tracks and boreal forest rainbelts only in eastern Canada. In contrast, this coincidence does not occur in northern Europe, eastern Siberia, and the Alaska–Pacific coast, because boreal forest rainbelts in these regions are located farther south of strong Arctic westerlies and ahead of high-latitude troughs over central Eurasia, the Bering Sea, the Labrador Sea, and the Norwegian Sea. Therefore, instead of baroclinicity along strong Arctic westerlies, favorable environments for the formation of minor storm tracks are developed by positive vorticity advections ahead of these high-latitude troughs. The water vapor budget analyses performed with NCEP and Goddard Earth Observing System (GEOS-1) reanalyses show that the boreal forest rainbelts are essentially maintained by the convergence of water vapor flux associated with transient disturbances at high latitudes.
Abstract
It is not unreasonable to expect that boreal forests that exist along 60°N in the Eurasian and North American continents were created and are maintained by warm seasonal rainfall. As revealed from satellite observations and various precipitation sources, zonally elongated rainbelts appear along these forests. Previous studies show that a relationship may exist between the frontal zone along the Arctic seaboard and regional patterns of high-latitude precipitation. It was observed by this study that baroclinic zones associated with strong Arctic westerlies coincide with minor storm tracks and boreal forest rainbelts only in eastern Canada. In contrast, this coincidence does not occur in northern Europe, eastern Siberia, and the Alaska–Pacific coast, because boreal forest rainbelts in these regions are located farther south of strong Arctic westerlies and ahead of high-latitude troughs over central Eurasia, the Bering Sea, the Labrador Sea, and the Norwegian Sea. Therefore, instead of baroclinicity along strong Arctic westerlies, favorable environments for the formation of minor storm tracks are developed by positive vorticity advections ahead of these high-latitude troughs. The water vapor budget analyses performed with NCEP and Goddard Earth Observing System (GEOS-1) reanalyses show that the boreal forest rainbelts are essentially maintained by the convergence of water vapor flux associated with transient disturbances at high latitudes.
Abstract
Indochina is located between two extensively researched components of the Asian monsoon system: the Indian subcontinent and southeast–east Asia. Highly correlated with the National Oceanic and Atmospheric Administration Niño-3 sea surface temperatures, the interannual variation of Indochina monsoon rainfall is caused by a mechanism different from the two aforementioned regions. This mechanism consists of two elements: 1) the interannual modulation of the occurrence frequency of westward-propagating weather disturbances in the South China Sea–western tropical Pacific by an anomalous short-wave train emanating from the western tropical Pacific, and 2) an east–west interannual seesaw of the global divergent water vapor flux induced by the interannual variation in the global divergent circulation. An effort is made in this study to illustrate this mechanism.
Abstract
Indochina is located between two extensively researched components of the Asian monsoon system: the Indian subcontinent and southeast–east Asia. Highly correlated with the National Oceanic and Atmospheric Administration Niño-3 sea surface temperatures, the interannual variation of Indochina monsoon rainfall is caused by a mechanism different from the two aforementioned regions. This mechanism consists of two elements: 1) the interannual modulation of the occurrence frequency of westward-propagating weather disturbances in the South China Sea–western tropical Pacific by an anomalous short-wave train emanating from the western tropical Pacific, and 2) an east–west interannual seesaw of the global divergent water vapor flux induced by the interannual variation in the global divergent circulation. An effort is made in this study to illustrate this mechanism.
Abstract
The Amazon rain forest may undergo significant change in response to future climate change. To determine the likelihood and causes of such changes, the authors analyzed the output of 24 models from the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) and a dynamic vegetation model, Vegetation–Global–Atmosphere–Soil (VEGAS), driven by these climate output. Their results suggest that the core of the Amazon rain forest should remain largely stable because rainfall in the core of the basin is projected to increase in nearly all models. However, the periphery, notably the southern edge of Amazonia and farther south into central Brazil (SAB), is in danger of drying out, driven by two main processes. First, a decline in precipitation of 11% during the southern Amazonia’s dry season (May–September) reduces soil moisture. Two dynamical mechanisms may explain the forecast reduction in dry season rainfall: 1) a general subtropical drying under global warming when the dry season southern Amazon basin is under the control of subtropical high pressure and 2) a stronger north–south tropical Atlantic sea surface temperature gradient and, to a lesser degree, a warmer eastern equatorial Pacific. The drying corresponds to a lengthening of the dry season by approximately 10 days. The decline in soil moisture occurs despite an increase in precipitation during the wet season, because of nonlinear responses in hydrology associated with the decline in dry season precipitation, ecosystem dynamics, and an increase in evaporative demand due to the general warming. In terms of ecosystem response, higher maintenance cost and reduced productivity under warming may also have additional adverse impact. Although the IPCC models have substantial intermodel variation in precipitation change, these latter two hydroecological effects are highly robust because of the general warming simulated by all models. As a result, when forced by these climate projections, a dynamic vegetation model VEGAS projects an enhancement of fire risk by 20%–30% in the SAB region. Fire danger reaches its peak in Amazonia during the dry season, and this danger is expected to increase primarily because of the reduction in soil moisture and the decrease in dry season rainfall. VEGAS also projects a reduction of about 0.77 in leaf area index (LAI) over the SAB region. The vegetation response may be partially mediated by the CO2 fertilization effect, because a sensitivity experiment without CO2 fertilization shows a higher 0.89 decrease in LAI. Southern Amazonia is currently under intense human influence as a result of deforestation and land-use change. Should this direct human impact continue at present rates, added pressure to the region’s ecosystems from climate change may subject the region to profound changes in the twenty-first century.
Abstract
The Amazon rain forest may undergo significant change in response to future climate change. To determine the likelihood and causes of such changes, the authors analyzed the output of 24 models from the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) and a dynamic vegetation model, Vegetation–Global–Atmosphere–Soil (VEGAS), driven by these climate output. Their results suggest that the core of the Amazon rain forest should remain largely stable because rainfall in the core of the basin is projected to increase in nearly all models. However, the periphery, notably the southern edge of Amazonia and farther south into central Brazil (SAB), is in danger of drying out, driven by two main processes. First, a decline in precipitation of 11% during the southern Amazonia’s dry season (May–September) reduces soil moisture. Two dynamical mechanisms may explain the forecast reduction in dry season rainfall: 1) a general subtropical drying under global warming when the dry season southern Amazon basin is under the control of subtropical high pressure and 2) a stronger north–south tropical Atlantic sea surface temperature gradient and, to a lesser degree, a warmer eastern equatorial Pacific. The drying corresponds to a lengthening of the dry season by approximately 10 days. The decline in soil moisture occurs despite an increase in precipitation during the wet season, because of nonlinear responses in hydrology associated with the decline in dry season precipitation, ecosystem dynamics, and an increase in evaporative demand due to the general warming. In terms of ecosystem response, higher maintenance cost and reduced productivity under warming may also have additional adverse impact. Although the IPCC models have substantial intermodel variation in precipitation change, these latter two hydroecological effects are highly robust because of the general warming simulated by all models. As a result, when forced by these climate projections, a dynamic vegetation model VEGAS projects an enhancement of fire risk by 20%–30% in the SAB region. Fire danger reaches its peak in Amazonia during the dry season, and this danger is expected to increase primarily because of the reduction in soil moisture and the decrease in dry season rainfall. VEGAS also projects a reduction of about 0.77 in leaf area index (LAI) over the SAB region. The vegetation response may be partially mediated by the CO2 fertilization effect, because a sensitivity experiment without CO2 fertilization shows a higher 0.89 decrease in LAI. Southern Amazonia is currently under intense human influence as a result of deforestation and land-use change. Should this direct human impact continue at present rates, added pressure to the region’s ecosystems from climate change may subject the region to profound changes in the twenty-first century.
Abstract
In an approach termed the PER method, where the key input variables are observed precipitation P and runoff R and estimated evaporation, the authors apply the basin water budget equation to diagnose the long-term variability of the total terrestrial water storage (TWS). Unlike the typical offline land surface model estimate where only atmospheric variables are used as input, the direct use of observed runoff in the PER method imposes an important constraint on the diagnosed TWS. Although there is a lack of basin-scale observations of evaporation, the tendency of E to have significantly less variability than the difference between precipitation and runoff (P − R) minimizes the uncertainties originating from estimated evaporation. Compared to the more traditional method using atmospheric moisture convergence (MC) minus R (MCR method), the use of observed precipitation in the PER method is expected to lead to general improvement, especially in regions where atmospheric radiosonde data are too sparse to constrain the atmospheric model analyzed MC, such as in the remote tropics.
TWS was diagnosed using the PER method for the Amazon (1970–2006) and the Mississippi basin (1928–2006) and compared with the MCR method, land surface model and reanalyses, and NASA’s Gravity Recovery and Climate Experiment (GRACE) satellite gravity data. The seasonal cycle of diagnosed TWS over the Amazon is about 300 mm. The interannual TWS variability in these two basins is 100–200 mm, but multidecadal changes can be as large as 600–800 mm. Major droughts, such as the Dust Bowl period, had large impacts, with water storage depleted by 500 mm over a decade. Within the short period 2003–06 when GRACE data were available, PER and GRACE show good agreement both for seasonal cycle and interannual variability, providing potential to cross validate each other. In contrast, land surface model results are significantly smaller than PER and GRACE, especially toward longer time scales. While the authors currently lack independent means to verify these long-term changes, simple error analysis using three precipitation datasets and three evaporation estimates suggest that the multidecadal amplitude can be uncertain up to a factor of 2, while the agreement is high on interannual time scales. The large TWS variability implies the remarkable capacity of land surface in storing and taking up water that may be underrepresented in models. The results also suggest the existence of water storage memories on multiyear time scales, significantly longer than typically assumed seasonal time scales associated with surface soil moisture.
Abstract
In an approach termed the PER method, where the key input variables are observed precipitation P and runoff R and estimated evaporation, the authors apply the basin water budget equation to diagnose the long-term variability of the total terrestrial water storage (TWS). Unlike the typical offline land surface model estimate where only atmospheric variables are used as input, the direct use of observed runoff in the PER method imposes an important constraint on the diagnosed TWS. Although there is a lack of basin-scale observations of evaporation, the tendency of E to have significantly less variability than the difference between precipitation and runoff (P − R) minimizes the uncertainties originating from estimated evaporation. Compared to the more traditional method using atmospheric moisture convergence (MC) minus R (MCR method), the use of observed precipitation in the PER method is expected to lead to general improvement, especially in regions where atmospheric radiosonde data are too sparse to constrain the atmospheric model analyzed MC, such as in the remote tropics.
TWS was diagnosed using the PER method for the Amazon (1970–2006) and the Mississippi basin (1928–2006) and compared with the MCR method, land surface model and reanalyses, and NASA’s Gravity Recovery and Climate Experiment (GRACE) satellite gravity data. The seasonal cycle of diagnosed TWS over the Amazon is about 300 mm. The interannual TWS variability in these two basins is 100–200 mm, but multidecadal changes can be as large as 600–800 mm. Major droughts, such as the Dust Bowl period, had large impacts, with water storage depleted by 500 mm over a decade. Within the short period 2003–06 when GRACE data were available, PER and GRACE show good agreement both for seasonal cycle and interannual variability, providing potential to cross validate each other. In contrast, land surface model results are significantly smaller than PER and GRACE, especially toward longer time scales. While the authors currently lack independent means to verify these long-term changes, simple error analysis using three precipitation datasets and three evaporation estimates suggest that the multidecadal amplitude can be uncertain up to a factor of 2, while the agreement is high on interannual time scales. The large TWS variability implies the remarkable capacity of land surface in storing and taking up water that may be underrepresented in models. The results also suggest the existence of water storage memories on multiyear time scales, significantly longer than typically assumed seasonal time scales associated with surface soil moisture.
Abstract
A simple method was developed to forecast 3- and 6-month standardized precipitation indices (SPIs) for the prediction of meteorological drought over the contiguous United States based on precipitation seasonal forecasts from the NCEP Climate Forecast System (CFS). Before predicting SPI, the precipitation (P) forecasts from the coarse-resolution CFS global model were bias corrected and downscaled to a regional grid of 50 km. The downscaled CFS P forecasts, out to 9 months, were appended to the P analyses to form an extended P dataset. The SPIs were calculated from this new time series. Five downscaling methods were tested: 1) bilinear interpolation; 2) a bias correction and spatial downscaling (BCSD) method based on the probability distribution functions; 3) a conditional probability estimation approach using the mean P ensemble forecasts developed by J. Schaake, 4) a Bayesian approach that bias corrects and downscales P using all ensemble forecast members, as developed by the Princeton University group; and 5) multimethod ensemble as the equally weighted mean of the BCSD, Schaake, and Bayesian forecasts. For initial conditions from April to May, statistical downscaling methods were compared with dynamic downscaling based on the NCEP regional spectral model and forecasts from a high-resolution CFS T382 model. The skill is regionally and seasonally dependent. Overall, the 6-month SPI is skillful out to 3–4 months. For the first 3-month lead times, forecast skill comes from the P analyses prior to the forecast time. After 3 months, the multimethod ensemble has small advantages, but forecast skill may be too low to be useful in practice.
Abstract
A simple method was developed to forecast 3- and 6-month standardized precipitation indices (SPIs) for the prediction of meteorological drought over the contiguous United States based on precipitation seasonal forecasts from the NCEP Climate Forecast System (CFS). Before predicting SPI, the precipitation (P) forecasts from the coarse-resolution CFS global model were bias corrected and downscaled to a regional grid of 50 km. The downscaled CFS P forecasts, out to 9 months, were appended to the P analyses to form an extended P dataset. The SPIs were calculated from this new time series. Five downscaling methods were tested: 1) bilinear interpolation; 2) a bias correction and spatial downscaling (BCSD) method based on the probability distribution functions; 3) a conditional probability estimation approach using the mean P ensemble forecasts developed by J. Schaake, 4) a Bayesian approach that bias corrects and downscales P using all ensemble forecast members, as developed by the Princeton University group; and 5) multimethod ensemble as the equally weighted mean of the BCSD, Schaake, and Bayesian forecasts. For initial conditions from April to May, statistical downscaling methods were compared with dynamic downscaling based on the NCEP regional spectral model and forecasts from a high-resolution CFS T382 model. The skill is regionally and seasonally dependent. Overall, the 6-month SPI is skillful out to 3–4 months. For the first 3-month lead times, forecast skill comes from the P analyses prior to the forecast time. After 3 months, the multimethod ensemble has small advantages, but forecast skill may be too low to be useful in practice.
Abstract
The occurrence frequency of the east Asian cold surge exhibits an interannual variation in concert with the El Niño–Southern Oscillation (ENSO) cycle. That is, the cold surge occurs more (less) frequently during warm (cold) ENSO winters. Because the cold surge high–low dipoles are coupled with the upper-level synoptic short waves, any mechanism modulating the activity of these waves would affect the cold surge activity. The streamfunction budget analysis in the short-wave regime indicates that the development of the cold surge short-wave train over east Asia and the northwest Pacific is modulated by the North Pacific ENSO short-wave train. Due to the coupling between the upper-level cold surge short-wave train and the surface cold surge dipole, it is inferred from this streamfunction budget analysis that the interannual variation of the cold surge occurrence frequency is a result of this modulation.
Abstract
The occurrence frequency of the east Asian cold surge exhibits an interannual variation in concert with the El Niño–Southern Oscillation (ENSO) cycle. That is, the cold surge occurs more (less) frequently during warm (cold) ENSO winters. Because the cold surge high–low dipoles are coupled with the upper-level synoptic short waves, any mechanism modulating the activity of these waves would affect the cold surge activity. The streamfunction budget analysis in the short-wave regime indicates that the development of the cold surge short-wave train over east Asia and the northwest Pacific is modulated by the North Pacific ENSO short-wave train. Due to the coupling between the upper-level cold surge short-wave train and the surface cold surge dipole, it is inferred from this streamfunction budget analysis that the interannual variation of the cold surge occurrence frequency is a result of this modulation.
Abstract
Using multiple observational and model datasets, the authors document a strengthening relationship between boreal winter sea surface temperature anomalies (SSTAs) in the western North Pacific (WNP) and the development of the El Niño–Southern Oscillation (ENSO) in the following year. The increased WNP–ENSO association emerged in the mid-twentieth century and has grown through the present, reaching correlation coefficients as high as ~0.70 in recent decades. Fully coupled climate experiments with the Community Earth System Model, version 1 (CESM1), replicate the WNP–ENSO association and indicate that greenhouse gases (GHGs) are largely responsible for this observed increase. The authors speculate that shifts in the location of the largest positive SST trends between the subtropical and tropical western Pacific impact the low-level circulation in a manner that reinforces the link between the WNP and the development of ENSO. A strengthened GHG-driven relationship with the WNP provides an example of how anthropogenic climate change may directly influence one of the most prominent patterns of natural climate variability, ENSO, and potentially improve the skill of intraseasonal-to-interannual climate prediction.
Abstract
Using multiple observational and model datasets, the authors document a strengthening relationship between boreal winter sea surface temperature anomalies (SSTAs) in the western North Pacific (WNP) and the development of the El Niño–Southern Oscillation (ENSO) in the following year. The increased WNP–ENSO association emerged in the mid-twentieth century and has grown through the present, reaching correlation coefficients as high as ~0.70 in recent decades. Fully coupled climate experiments with the Community Earth System Model, version 1 (CESM1), replicate the WNP–ENSO association and indicate that greenhouse gases (GHGs) are largely responsible for this observed increase. The authors speculate that shifts in the location of the largest positive SST trends between the subtropical and tropical western Pacific impact the low-level circulation in a manner that reinforces the link between the WNP and the development of ENSO. A strengthened GHG-driven relationship with the WNP provides an example of how anthropogenic climate change may directly influence one of the most prominent patterns of natural climate variability, ENSO, and potentially improve the skill of intraseasonal-to-interannual climate prediction.
Abstract
Better understanding of factors that control the global carbon cycle could increase confidence in climate projections. Previous studies found good correlation between the growth rate of atmospheric CO2 concentration and El Niño–Southern Oscillation (ENSO). The growth rate of atmospheric CO2 increases during El Niño but decreases during La Niña. In this study, long-term simulations of the Earth system models (ESMs) in phase 5 of the Coupled Model Intercomparison Project archive were used to examine the interannual carbon flux variability associated with ENSO. The ESMs simulate the relationship reasonably well with a delay of several months between ENSO and the changes in atmospheric CO2. The increase in atmospheric CO2 associated with El Niño is mostly caused by decreasing net primary production (NPP) in the ESMs. It is suggested that NPP anomalies over South Asia are at their maxima during boreal spring; therefore, the increase in CO2 concentration lags 4–5 months behind the peak phase of El Niño. The decrease in NPP during El Niño may be caused by decreased precipitation and increased temperature over tropical regions. Furthermore, systematic errors may exist in the ESM-simulated temperature responses to ENSO phases over tropical land areas, and these errors may lead to an overestimation of ENSO-related NPP anomalies. In contrast, carbon fluxes from heterotrophic respiration and natural fires are likely underestimated in the ESMs compared with offline model results and observational estimates, respectively. These uncertainties should be considered in long-term projections that include climate–carbon feedbacks.
Abstract
Better understanding of factors that control the global carbon cycle could increase confidence in climate projections. Previous studies found good correlation between the growth rate of atmospheric CO2 concentration and El Niño–Southern Oscillation (ENSO). The growth rate of atmospheric CO2 increases during El Niño but decreases during La Niña. In this study, long-term simulations of the Earth system models (ESMs) in phase 5 of the Coupled Model Intercomparison Project archive were used to examine the interannual carbon flux variability associated with ENSO. The ESMs simulate the relationship reasonably well with a delay of several months between ENSO and the changes in atmospheric CO2. The increase in atmospheric CO2 associated with El Niño is mostly caused by decreasing net primary production (NPP) in the ESMs. It is suggested that NPP anomalies over South Asia are at their maxima during boreal spring; therefore, the increase in CO2 concentration lags 4–5 months behind the peak phase of El Niño. The decrease in NPP during El Niño may be caused by decreased precipitation and increased temperature over tropical regions. Furthermore, systematic errors may exist in the ESM-simulated temperature responses to ENSO phases over tropical land areas, and these errors may lead to an overestimation of ENSO-related NPP anomalies. In contrast, carbon fluxes from heterotrophic respiration and natural fires are likely underestimated in the ESMs compared with offline model results and observational estimates, respectively. These uncertainties should be considered in long-term projections that include climate–carbon feedbacks.
