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- Author or Editor: Yonggang Liu x
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Abstract
How the climate responded to orbital forcing during the Neoproterozoic snowball Earth events, the most extreme glaciations on Earth, is still unclear. Here, we investigate this problem using a climate model. To simplify the analysis, continents are removed. The results show that even in this simplified situation, the snowball Earth climate is sensitive to orbital configurations. The globally averaged annual surface temperature can differ by 2.4°C, and the maximum monthly mean temperature can differ by 3.7°C under different orbital configurations. Therefore, a snowball Earth could be deglaciated more easily in some orbital configurations than in others. The climatic effect of a particular orbital parameter is highly dependent on the values of other parameters. For example, the effect of obliquity on tropical surface temperature is generally small (<1°C), but it can become large (3.8°C) when eccentricity is large and the northern autumn occurs at perihelion (precession = 180°). Surprisingly, the global temperature is generally lower at high eccentricity than at near-zero eccentricity, even though the total insolation received by Earth is higher in the former than in the latter. Moreover, we find that the Milankovitch hypothesis is valid not only in the extratropical region, but also in the tropics; the snow thickness in the tropical region is inversely proportional to the maximum monthly insolation received in this region.
Abstract
How the climate responded to orbital forcing during the Neoproterozoic snowball Earth events, the most extreme glaciations on Earth, is still unclear. Here, we investigate this problem using a climate model. To simplify the analysis, continents are removed. The results show that even in this simplified situation, the snowball Earth climate is sensitive to orbital configurations. The globally averaged annual surface temperature can differ by 2.4°C, and the maximum monthly mean temperature can differ by 3.7°C under different orbital configurations. Therefore, a snowball Earth could be deglaciated more easily in some orbital configurations than in others. The climatic effect of a particular orbital parameter is highly dependent on the values of other parameters. For example, the effect of obliquity on tropical surface temperature is generally small (<1°C), but it can become large (3.8°C) when eccentricity is large and the northern autumn occurs at perihelion (precession = 180°). Surprisingly, the global temperature is generally lower at high eccentricity than at near-zero eccentricity, even though the total insolation received by Earth is higher in the former than in the latter. Moreover, we find that the Milankovitch hypothesis is valid not only in the extratropical region, but also in the tropics; the snow thickness in the tropical region is inversely proportional to the maximum monthly insolation received in this region.
Abstract
The across-shelf structures of the ocean circulation and the associated sea surface height (SSH) variability are examined on the west Florida shelf (WFS) for the 3-yr interval from September 1998 to December 2001. Five sets of characteristic circulation patterns are extracted from 2-day, low-pass-filtered data using the self-organizing map: extreme upwelling and downwelling structures with strong currents, asymmetric upwelling and downwelling structures with moderate currents, and a set of transitional structures with weak currents. The temporal variations of these structures are coherent with the local winds on synoptic weather time scales. On seasonal time scales they are related to both the local winds and the water density variations. The circulation is predominantly upwelling during autumn to spring months (October–April) and downwelling during summer months (June–September). Coastal sea level fluctuations are related to both the dynamical responses of the inner shelf circulation to meteorological forcing and the offshore SSH. On long time scales, the offshore SSH variations appear to dominate, whereas on synoptic weather time scales, the inner shelf wind-driven circulation responses are largest. The across-shelf distribution of SSH is estimated from the velocity, hydrography, wind, and coastal sea level data, and the results are compared with satellite altimetry data, thereby providing a means for calibrating satellite altimetry on the shelf.
Abstract
The across-shelf structures of the ocean circulation and the associated sea surface height (SSH) variability are examined on the west Florida shelf (WFS) for the 3-yr interval from September 1998 to December 2001. Five sets of characteristic circulation patterns are extracted from 2-day, low-pass-filtered data using the self-organizing map: extreme upwelling and downwelling structures with strong currents, asymmetric upwelling and downwelling structures with moderate currents, and a set of transitional structures with weak currents. The temporal variations of these structures are coherent with the local winds on synoptic weather time scales. On seasonal time scales they are related to both the local winds and the water density variations. The circulation is predominantly upwelling during autumn to spring months (October–April) and downwelling during summer months (June–September). Coastal sea level fluctuations are related to both the dynamical responses of the inner shelf circulation to meteorological forcing and the offshore SSH. On long time scales, the offshore SSH variations appear to dominate, whereas on synoptic weather time scales, the inner shelf wind-driven circulation responses are largest. The across-shelf distribution of SSH is estimated from the velocity, hydrography, wind, and coastal sea level data, and the results are compared with satellite altimetry data, thereby providing a means for calibrating satellite altimetry on the shelf.
Abstract
This paper addresses a bias problem in the estimate of wavelet power spectra for atmospheric and oceanic datasets. For a time series comprised of sine waves with the same amplitude at different frequencies the conventionally adopted wavelet method does not produce a spectrum with identical peaks, in contrast to a Fourier analysis. The wavelet power spectrum in this definition, that is, the transform coefficient squared (to within a constant factor), is equivalent to the integration of energy (in physical space) over the influence period (time scale) the series spans. Thus, a physically consistent definition of energy for the wavelet power spectrum should be the transform coefficient squared divided by the scale it associates. Such adjusted wavelet power spectrum results in a substantial improvement in the spectral estimate, allowing for a comparison of the spectral peaks across scales. The improvement is validated with an artificial time series and a real coastal sea level record. Also examined is the previous example of the wavelet analysis of the Niño-3 SST data.
Abstract
This paper addresses a bias problem in the estimate of wavelet power spectra for atmospheric and oceanic datasets. For a time series comprised of sine waves with the same amplitude at different frequencies the conventionally adopted wavelet method does not produce a spectrum with identical peaks, in contrast to a Fourier analysis. The wavelet power spectrum in this definition, that is, the transform coefficient squared (to within a constant factor), is equivalent to the integration of energy (in physical space) over the influence period (time scale) the series spans. Thus, a physically consistent definition of energy for the wavelet power spectrum should be the transform coefficient squared divided by the scale it associates. Such adjusted wavelet power spectrum results in a substantial improvement in the spectral estimate, allowing for a comparison of the spectral peaks across scales. The improvement is validated with an artificial time series and a real coastal sea level record. Also examined is the previous example of the wavelet analysis of the Niño-3 SST data.
Abstract
North Africa was green during the mid-Holocene [about 6000 years ago (6 ka)] and emitted much less dust to the atmosphere than in the present day. Here we use a fully coupled atmosphere–ocean general circulation model, CESM1.2.2, to test the impact of dust reduction and greening of the Sahara on the Atlantic meridional overturning circulation (AMOC) during this period. Results show that dust removal leads to a decrease of AMOC by 6.2% while greening of the Sahara with 100% shrub (100% grass) cover causes an enhancement of the AMOC by 6.1% (4.8%). The AMOC is increased by 5.3% (2.3%) when both the dust reduction and green Sahara with 100% shrub (100% grass) are considered. The AMOC changes are primarily due to the precipitation change over the west subtropical North Atlantic, from where the salinity anomaly is advected to the deep-water formation region. Global-mean surface temperature increases by 0.09° and 0.40°C (0.25°C) when global dust is removed and when North Africa and the Arabian region are covered by shrub (grass), respectively, showing a dominating effect of vegetation over dust. The comparison between modeled and reconstructed sea surface temperature is improved when the effect of vegetation is considered. The results may have implications for climate impact of future wetting over North Africa, either through global warming or through building of solar farms and wind farms.
Abstract
North Africa was green during the mid-Holocene [about 6000 years ago (6 ka)] and emitted much less dust to the atmosphere than in the present day. Here we use a fully coupled atmosphere–ocean general circulation model, CESM1.2.2, to test the impact of dust reduction and greening of the Sahara on the Atlantic meridional overturning circulation (AMOC) during this period. Results show that dust removal leads to a decrease of AMOC by 6.2% while greening of the Sahara with 100% shrub (100% grass) cover causes an enhancement of the AMOC by 6.1% (4.8%). The AMOC is increased by 5.3% (2.3%) when both the dust reduction and green Sahara with 100% shrub (100% grass) are considered. The AMOC changes are primarily due to the precipitation change over the west subtropical North Atlantic, from where the salinity anomaly is advected to the deep-water formation region. Global-mean surface temperature increases by 0.09° and 0.40°C (0.25°C) when global dust is removed and when North Africa and the Arabian region are covered by shrub (grass), respectively, showing a dominating effect of vegetation over dust. The comparison between modeled and reconstructed sea surface temperature is improved when the effect of vegetation is considered. The results may have implications for climate impact of future wetting over North Africa, either through global warming or through building of solar farms and wind farms.
Abstract
There are several high-resolution (1–12 km) gridded precipitation datasets covering the interior western United States. This study cross validates seasonal orographic precipitation estimates from the Snowpack Telemetry (SNOTEL) network; the national hourly multisensor precipitation analysis Stage IV dataset (NCEP IV); four gauge-driven gridded datasets; and a 10-yr, 4-km, convection-permitting Weather Research and Forecasting (WRF) Model simulation. The NCEP IV dataset, which uses the NEXRAD network and precipitation gauges, is challenged in this region because of blockage and lack of low-level radar coverage in complex terrain. The gauge-driven gridded datasets, which statistically interpolate gauge measurements over complex terrain to better estimate orographic precipitation, are challenged by the highly heterogeneous, weather-dependent nature of precipitation in complex terrain at scales finer than can be resolved by the gauge network, such as the SNOTEL network. Gauge-driven gridded precipitation estimates disagree in areas where SNOTEL gauges are sparse, especially at higher elevations. The WRF simulation captures wintertime orographic precipitation distribution and amount well, and biases over specific mountain ranges are identical to those in an independent WRF simulation, suggesting that these biases are at least partly due to errors in the snowfall measurements or the gridding of these measurements. The substantial disagreement between WRF and the gridded datasets over some mountains may motivate reevaluation of some gauge records and installation of new SNOTEL gauges in regions marked by large discrepancies between modeled and gauge-driven precipitation estimates.
Abstract
There are several high-resolution (1–12 km) gridded precipitation datasets covering the interior western United States. This study cross validates seasonal orographic precipitation estimates from the Snowpack Telemetry (SNOTEL) network; the national hourly multisensor precipitation analysis Stage IV dataset (NCEP IV); four gauge-driven gridded datasets; and a 10-yr, 4-km, convection-permitting Weather Research and Forecasting (WRF) Model simulation. The NCEP IV dataset, which uses the NEXRAD network and precipitation gauges, is challenged in this region because of blockage and lack of low-level radar coverage in complex terrain. The gauge-driven gridded datasets, which statistically interpolate gauge measurements over complex terrain to better estimate orographic precipitation, are challenged by the highly heterogeneous, weather-dependent nature of precipitation in complex terrain at scales finer than can be resolved by the gauge network, such as the SNOTEL network. Gauge-driven gridded precipitation estimates disagree in areas where SNOTEL gauges are sparse, especially at higher elevations. The WRF simulation captures wintertime orographic precipitation distribution and amount well, and biases over specific mountain ranges are identical to those in an independent WRF simulation, suggesting that these biases are at least partly due to errors in the snowfall measurements or the gridding of these measurements. The substantial disagreement between WRF and the gridded datasets over some mountains may motivate reevaluation of some gauge records and installation of new SNOTEL gauges in regions marked by large discrepancies between modeled and gauge-driven precipitation estimates.
Abstract
Two high-resolution (4 km) regional climate simulations over a 10-yr period are conducted to study the changes in wintertime precipitation distribution across mountain ranges in the interior western United States (IWUS) in a warming climate. One simulation represents the current climate, and another represents an ~2050 climate using a pseudo–global warming approach. The climate perturbations are derived from the ensemble mean of 15 global climate models from phase 5 of the Coupled Model Intercomparison Project (CMIP5). These simulations provide an estimate of average changes in wintertime orographic precipitation enhancement and finescale distribution across mountain ranges. The variability in these changes among CMIP5 models is quantified using statistical downscaling relations between orographic precipitation distribution and upstream conditions, developed in Part I. The CMIP5 guidance indicates a robust warming signal (~2 K) over the IWUS by ~2050 but minor changes in relative humidity and cloud-base height. The IWUS simulations reveal a widespread increase in precipitation on account of higher precipitation rates during winter storms in this warmer climate. This precipitation increase is most significant over the mountains rather than on the surrounding plains. The increase in precipitation rate is largely due to an increase in low-level cross-mountain moisture transport. The application of the statistical relations indicates that individual CMIP5 models disagree about the magnitude and distribution of orographic precipitation change in the IWUS, although most agree with the ensemble-mean-predicted orographic precipitation increase.
Abstract
Two high-resolution (4 km) regional climate simulations over a 10-yr period are conducted to study the changes in wintertime precipitation distribution across mountain ranges in the interior western United States (IWUS) in a warming climate. One simulation represents the current climate, and another represents an ~2050 climate using a pseudo–global warming approach. The climate perturbations are derived from the ensemble mean of 15 global climate models from phase 5 of the Coupled Model Intercomparison Project (CMIP5). These simulations provide an estimate of average changes in wintertime orographic precipitation enhancement and finescale distribution across mountain ranges. The variability in these changes among CMIP5 models is quantified using statistical downscaling relations between orographic precipitation distribution and upstream conditions, developed in Part I. The CMIP5 guidance indicates a robust warming signal (~2 K) over the IWUS by ~2050 but minor changes in relative humidity and cloud-base height. The IWUS simulations reveal a widespread increase in precipitation on account of higher precipitation rates during winter storms in this warmer climate. This precipitation increase is most significant over the mountains rather than on the surrounding plains. The increase in precipitation rate is largely due to an increase in low-level cross-mountain moisture transport. The application of the statistical relations indicates that individual CMIP5 models disagree about the magnitude and distribution of orographic precipitation change in the IWUS, although most agree with the ensemble-mean-predicted orographic precipitation increase.
Abstract
This study analyzes the control of upstream conditions on the distribution of wintertime precipitation across mountain ranges in the interior western United States using 10 winters of high-resolution regional climate model data. Three mountain ranges, the Wind River Range, the Park Range, and the Teton Range, are selected to explore the statistical relations between the precipitation distribution and upstream wind, stability, and cloud conditions. A 4-km-resolution simulation is used for the former two ranges, and a 1.33-km-resolution simulation driven by the 4-km-resolution simulation is used for the Teton Range, which is smaller and steeper. Across all three mountain ranges, the dominant factor controlling precipitation is the mountain-normal low-level wind speed. Statistically, stronger wind results in heavier precipitation and a lower upwind precipitation fraction. The low-level wind generally veers with height during precipitation events, but the amount of veering does not unambiguously affect the precipitation distribution or intensity. The more the terrain blocks the upstream flow, the more the precipitation shifts toward the upstream side of the mountain and the weaker the overall precipitation rate is. A higher cloud-base temperature and a lower cloud-base height typically are associated with heavier precipitation. Deeper clouds tend to produce heavier precipitation and a slightly lower windward/leeward contrast. Convective precipitation proportionally falls more on the lee slopes than stratiform precipitation. The upstream and macroscale cloud conditions identified herein predict both the mean precipitation rate and the upwind precipitation fraction very well for the three ranges studied here.
Abstract
This study analyzes the control of upstream conditions on the distribution of wintertime precipitation across mountain ranges in the interior western United States using 10 winters of high-resolution regional climate model data. Three mountain ranges, the Wind River Range, the Park Range, and the Teton Range, are selected to explore the statistical relations between the precipitation distribution and upstream wind, stability, and cloud conditions. A 4-km-resolution simulation is used for the former two ranges, and a 1.33-km-resolution simulation driven by the 4-km-resolution simulation is used for the Teton Range, which is smaller and steeper. Across all three mountain ranges, the dominant factor controlling precipitation is the mountain-normal low-level wind speed. Statistically, stronger wind results in heavier precipitation and a lower upwind precipitation fraction. The low-level wind generally veers with height during precipitation events, but the amount of veering does not unambiguously affect the precipitation distribution or intensity. The more the terrain blocks the upstream flow, the more the precipitation shifts toward the upstream side of the mountain and the weaker the overall precipitation rate is. A higher cloud-base temperature and a lower cloud-base height typically are associated with heavier precipitation. Deeper clouds tend to produce heavier precipitation and a slightly lower windward/leeward contrast. Convective precipitation proportionally falls more on the lee slopes than stratiform precipitation. The upstream and macroscale cloud conditions identified herein predict both the mean precipitation rate and the upwind precipitation fraction very well for the three ranges studied here.
Abstract
Neural network analyses based on the self-organizing map (SOM) and the growing hierarchical self-organizing map (GHSOM) are used to examine patterns of the sea surface temperature (SST) variability on the West Florida Shelf from time series of daily SST maps from 1998 to 2002. Four characteristic SST patterns are extracted in the first-layer GHSOM array: winter and summer season patterns, and two transitional patterns. Three of them are further expanded in the second layer, yielding more detailed structures in these seasons. The winter pattern is one of low SST, with isotherms aligned approximately along isobaths. The summer pattern is one of high SST distributed in a horizontally uniform manner. The spring transition includes a midshelf cold tongue. Similar analyses performed on SST anomaly data provide further details of these seasonally varying patterns. It is demonstrated that the GHSOM analysis is more effective in extracting the inherent SST patterns than the widely used EOF method. The underlying patterns in a dataset can be visualized in the SOM array in the same form as the original data, while they can only be expressed in anomaly form in the EOF analysis. Some important features, such as asymmetric SST anomaly patterns of winter/summer and cold/warm tongues, can be revealed by the SOM array but cannot be identified in the lowest mode EOF patterns. Also, unlike the EOF or SOM techniques, the hierarchical structure in the input data can be extracted by the GHSOM analysis.
Abstract
Neural network analyses based on the self-organizing map (SOM) and the growing hierarchical self-organizing map (GHSOM) are used to examine patterns of the sea surface temperature (SST) variability on the West Florida Shelf from time series of daily SST maps from 1998 to 2002. Four characteristic SST patterns are extracted in the first-layer GHSOM array: winter and summer season patterns, and two transitional patterns. Three of them are further expanded in the second layer, yielding more detailed structures in these seasons. The winter pattern is one of low SST, with isotherms aligned approximately along isobaths. The summer pattern is one of high SST distributed in a horizontally uniform manner. The spring transition includes a midshelf cold tongue. Similar analyses performed on SST anomaly data provide further details of these seasonally varying patterns. It is demonstrated that the GHSOM analysis is more effective in extracting the inherent SST patterns than the widely used EOF method. The underlying patterns in a dataset can be visualized in the SOM array in the same form as the original data, while they can only be expressed in anomaly form in the EOF analysis. Some important features, such as asymmetric SST anomaly patterns of winter/summer and cold/warm tongues, can be revealed by the SOM array but cannot be identified in the lowest mode EOF patterns. Also, unlike the EOF or SOM techniques, the hierarchical structure in the input data can be extracted by the GHSOM analysis.
Abstract
It has been demonstrated previously that atmospheric dust loading during the Precambrian could have been an order of magnitude higher than in the present day and could have cooled the global climate by more than 10°C. Here, using the fully coupled atmosphere–ocean general circulation model CESM1.2.2, we determine whether such dust loading could have facilitated the formation of Neoproterozoic snowball Earth events. Our results indicate that global dust emission decreases as atmospheric CO2 concentration (pCO2) decreases due to increasing snow coverage, but atmospheric dust loading does not change or even increases due to decreasing precipitation and strengthening June–August (JJA) Hadley circulation. The latter lifts more dust particles to high altitude and thus increases the lifetime of these particles. As the climate becomes colder and the surface albedo higher, the cooling effect of dust becomes weaker; when the global mean surface temperature is approximately −13°C, dust has negligible cooling effect. The threshold pCO2 at which Earth enters a snowball state is between 280 to 140 ppmv when there is no dust, and is similar when there is relatively light dust loading (~4.4 times the present-day value). However, the threshold pCO2 decreases dramatically to between 70 and 35 ppmv when there is heavy dust loading (~33 times the present-day value), due to the decrease in planetary albedo, which increases the energy input into the climate system. Therefore, dust makes it more difficult for Earth to enter a snowball state.
Abstract
It has been demonstrated previously that atmospheric dust loading during the Precambrian could have been an order of magnitude higher than in the present day and could have cooled the global climate by more than 10°C. Here, using the fully coupled atmosphere–ocean general circulation model CESM1.2.2, we determine whether such dust loading could have facilitated the formation of Neoproterozoic snowball Earth events. Our results indicate that global dust emission decreases as atmospheric CO2 concentration (pCO2) decreases due to increasing snow coverage, but atmospheric dust loading does not change or even increases due to decreasing precipitation and strengthening June–August (JJA) Hadley circulation. The latter lifts more dust particles to high altitude and thus increases the lifetime of these particles. As the climate becomes colder and the surface albedo higher, the cooling effect of dust becomes weaker; when the global mean surface temperature is approximately −13°C, dust has negligible cooling effect. The threshold pCO2 at which Earth enters a snowball state is between 280 to 140 ppmv when there is no dust, and is similar when there is relatively light dust loading (~4.4 times the present-day value). However, the threshold pCO2 decreases dramatically to between 70 and 35 ppmv when there is heavy dust loading (~33 times the present-day value), due to the decrease in planetary albedo, which increases the energy input into the climate system. Therefore, dust makes it more difficult for Earth to enter a snowball state.
Abstract
To assess the spatial structures and temporal evolutions of distinct physical processes on the West Florida Shelf, patterns of ocean current variability are extracted from a joint HF radar and ADCP dataset acquired from August to September 2003 using Self-Organizing Map (SOM) analyses. Three separate ocean–atmosphere frequency bands are considered: semidiurnal, diurnal, and subtidal. The currents in the semidiurnal band are relatively homogeneous in space, barotropic, clockwise polarized, and have a neap-spring modulation consistent with semidiurnal tides. The currents in the diurnal band are less homogeneous, more baroclinic, and clockwise polarized, consistent with a combination of diurnal tides and near-inertial oscillations. The currents in the subtidal frequency band are stronger and with more complex patterns consistent with wind and buoyancy forcing. The SOM is shown to be a useful technique for extracting ocean current patterns with dynamically distinctive spatial and temporal structures sampled by HF radar and supporting in situ measurements.
Abstract
To assess the spatial structures and temporal evolutions of distinct physical processes on the West Florida Shelf, patterns of ocean current variability are extracted from a joint HF radar and ADCP dataset acquired from August to September 2003 using Self-Organizing Map (SOM) analyses. Three separate ocean–atmosphere frequency bands are considered: semidiurnal, diurnal, and subtidal. The currents in the semidiurnal band are relatively homogeneous in space, barotropic, clockwise polarized, and have a neap-spring modulation consistent with semidiurnal tides. The currents in the diurnal band are less homogeneous, more baroclinic, and clockwise polarized, consistent with a combination of diurnal tides and near-inertial oscillations. The currents in the subtidal frequency band are stronger and with more complex patterns consistent with wind and buoyancy forcing. The SOM is shown to be a useful technique for extracting ocean current patterns with dynamically distinctive spatial and temporal structures sampled by HF radar and supporting in situ measurements.
