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Abstract
Abstract
Abstract
This study is an extensive revision of the Climatic Research Unit (CRU) land station temperature database that is used to produce a gridbox dataset of 5° latitude × 5° longitude temperature anomalies. The new database comprises 5159 station records, of which 4167 have enough data for the 1961–90 period to calculate or estimate the necessary averages. Apart from the increase in station numbers compared to the earlier study in 1994, many station records have had their data replaced by newly homogenized series that have been produced by several recent studies. New versions of all the gridded datasets currently available on the CRU Web site (http://www.cru.uea.ac.uk) have been developed. This includes combinations with marine (sea surface temperature anomalies) data over the oceans and versions with adjustment of the variance of individual gridbox series to remove the effects of changing station numbers through time.
Hemispheric and global temperature averages for land areas developed with the new dataset differ slightly from those developed in 1994. Possible reasons for the differences between the new and the earlier analysis and those from the National Climatic Data Center and the Goddard Institute for Space Studies are discussed. Differences are greatest over the Southern Hemisphere and at the beginnings and ends of each time series and relate to gridbox sizes and data availability. The rate of annual warming for global land areas over the 1901–2000 period is estimated by least squares to be 0.07°C decade−1 (significant at better than the 99.9% level). Warming is not continuous but occurs principally over two periods (about 1920–45 and since 1975). Annual temperature series for the seven continents and the Arctic all show significant warming over the twentieth century, with significant (95%) warming for 1920–44 for North America, the Arctic, Africa, and South America, and all continents except Australia and the Antarctic since 1977. Cooling is significant during the intervening period (1945–76) for North America, the Arctic, and Africa.
Abstract
This study is an extensive revision of the Climatic Research Unit (CRU) land station temperature database that is used to produce a gridbox dataset of 5° latitude × 5° longitude temperature anomalies. The new database comprises 5159 station records, of which 4167 have enough data for the 1961–90 period to calculate or estimate the necessary averages. Apart from the increase in station numbers compared to the earlier study in 1994, many station records have had their data replaced by newly homogenized series that have been produced by several recent studies. New versions of all the gridded datasets currently available on the CRU Web site (http://www.cru.uea.ac.uk) have been developed. This includes combinations with marine (sea surface temperature anomalies) data over the oceans and versions with adjustment of the variance of individual gridbox series to remove the effects of changing station numbers through time.
Hemispheric and global temperature averages for land areas developed with the new dataset differ slightly from those developed in 1994. Possible reasons for the differences between the new and the earlier analysis and those from the National Climatic Data Center and the Goddard Institute for Space Studies are discussed. Differences are greatest over the Southern Hemisphere and at the beginnings and ends of each time series and relate to gridbox sizes and data availability. The rate of annual warming for global land areas over the 1901–2000 period is estimated by least squares to be 0.07°C decade−1 (significant at better than the 99.9% level). Warming is not continuous but occurs principally over two periods (about 1920–45 and since 1975). Annual temperature series for the seven continents and the Arctic all show significant warming over the twentieth century, with significant (95%) warming for 1920–44 for North America, the Arctic, Africa, and South America, and all continents except Australia and the Antarctic since 1977. Cooling is significant during the intervening period (1945–76) for North America, the Arctic, and Africa.
Abstract
There has been increasing demand in Australia for extended-range forecasts of extreme heat events. An assessment is made of the subseasonal experimental guidance provided by the Bureau of Meteorology’s seasonal prediction system, Predictive Ocean Atmosphere Model for Australia (POAMA, version 2), for the three most extreme heat events over Australia in 2013, which occurred in January, March, and September. The impacts of these events included devastating bushfires and damage to crops. The outlooks performed well for January and September, with forecasts indicating increased odds of top-decile maximum temperature over most affected areas at least one week in advance for the fortnightly averaged periods at the start of the heat waves and for forecasts of the months of January and September. The March event was more localized, affecting southern Australia. Although the anomalously high sea surface temperature around southern Australia in March (a potential source of predictability) was correctly forecast, the forecast of high temperatures over the mainland was restricted to the coastline. September was associated with strong forcing from some large-scale atmospheric climate drivers known to increase the chance of having more extreme temperatures over parts of Australia. POAMA-2 was able to forecast the sense of these drivers at least one week in advance, but their magnitude was weaker than observed. The reasonably good temperature forecasts for September are likely due to the model being able to forecast the important climate drivers and their teleconnection to Australian climate. This study adds to the growing evidence that there is significant potential to extend and augment traditional weather forecast guidance for extreme events to include longer-lead probabilistic information.
Abstract
There has been increasing demand in Australia for extended-range forecasts of extreme heat events. An assessment is made of the subseasonal experimental guidance provided by the Bureau of Meteorology’s seasonal prediction system, Predictive Ocean Atmosphere Model for Australia (POAMA, version 2), for the three most extreme heat events over Australia in 2013, which occurred in January, March, and September. The impacts of these events included devastating bushfires and damage to crops. The outlooks performed well for January and September, with forecasts indicating increased odds of top-decile maximum temperature over most affected areas at least one week in advance for the fortnightly averaged periods at the start of the heat waves and for forecasts of the months of January and September. The March event was more localized, affecting southern Australia. Although the anomalously high sea surface temperature around southern Australia in March (a potential source of predictability) was correctly forecast, the forecast of high temperatures over the mainland was restricted to the coastline. September was associated with strong forcing from some large-scale atmospheric climate drivers known to increase the chance of having more extreme temperatures over parts of Australia. POAMA-2 was able to forecast the sense of these drivers at least one week in advance, but their magnitude was weaker than observed. The reasonably good temperature forecasts for September are likely due to the model being able to forecast the important climate drivers and their teleconnection to Australian climate. This study adds to the growing evidence that there is significant potential to extend and augment traditional weather forecast guidance for extreme events to include longer-lead probabilistic information.
Abstract
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Abstract
A basin-scale, reduced-gravity model is used to study how drifter launch strategies affect the accuracy of Eulerian velocity fields reconstructed from limited Lagrangian data. Optimal dispersion launch sites are found by tracking strongly hyperbolic singular points in the flow field. Lagrangian data from drifters launched from such locations are found to provide significant improvement in the reconstruction accuracy over similar but randomly located initial deployments. The eigenvalues of the hyperbolic singular points in the flow field determine the intensity of the local particle dispersion and thereby provide a natural timescale for initializing subsequent launches. Aligning the initial drifter launch in each site along an outflowing manifold ensures both high initial particle dispersion and the eventual sampling of regions of high kinetic energy, two factors that substantially affect the accuracy of the Eulerian reconstruction. Reconstruction error is reduced by a factor of ∼2.5 by using a continual launch strategy based on both the local stretching rates and the outflowing directions of two strong saddles located in the dynamically active region south of the central jet. Notably, a majority of those randomly chosen launch sites that produced the most accurate reconstructions also sampled the local manifold structure.
Abstract
A basin-scale, reduced-gravity model is used to study how drifter launch strategies affect the accuracy of Eulerian velocity fields reconstructed from limited Lagrangian data. Optimal dispersion launch sites are found by tracking strongly hyperbolic singular points in the flow field. Lagrangian data from drifters launched from such locations are found to provide significant improvement in the reconstruction accuracy over similar but randomly located initial deployments. The eigenvalues of the hyperbolic singular points in the flow field determine the intensity of the local particle dispersion and thereby provide a natural timescale for initializing subsequent launches. Aligning the initial drifter launch in each site along an outflowing manifold ensures both high initial particle dispersion and the eventual sampling of regions of high kinetic energy, two factors that substantially affect the accuracy of the Eulerian reconstruction. Reconstruction error is reduced by a factor of ∼2.5 by using a continual launch strategy based on both the local stretching rates and the outflowing directions of two strong saddles located in the dynamically active region south of the central jet. Notably, a majority of those randomly chosen launch sites that produced the most accurate reconstructions also sampled the local manifold structure.
Abstract
This study focuses on cloudy atmosphere state estimation from high-resolution visible and infrared satellite remote sensing measurements and a mesoscale model with explicit cloud prediction. The cloud state is defined as 3D spatially distributed hydrometeors characterized with microphysical properties: mixing ratio, number concentration, and size distribution. The Geostationary Operational Environmental Satellite-9 (GOES-9) imager visible and infrared measurements were used in a new four-dimensional variational data assimilation (4DVAR) mesoscale algorithm for a warm continental stratus cloud system case to test the impact of these observations on the cloud simulation. The new data assimilation algorithm includes the Regional Atmospheric Modeling System (RAMS) with explicit cloud state prediction, the associated adjoint system, and an observational operator for forward and adjoint integrations of the GOES radiances. The results show positive impact of GOES imager measurements on the 3D cloud short-term simulation during and after the assimilation. The impact was achieved through sensitivity of the radiances to the cloud droplet mixing ratio at observation time and a 4D correlation between the cloud and atmospheric thermal and dynamical environment in the forecast model. The dynamical response to the radiance observations was through enhanced large mesoscale vertical mixing while horizontal advection was weak in the case of stable continental stratus evolution.
Although the current experiments show measurable positive impact of the cloudy radiance measurements on the stratus cloud simulation, they clearly suggest the need to further address the problem of negative cloud cover forecast errors. These errors were only weakly corrected in the current study because of the small sensitivity of the visible and infrared window radiances to the cloud-free atmosphere.
Abstract
This study focuses on cloudy atmosphere state estimation from high-resolution visible and infrared satellite remote sensing measurements and a mesoscale model with explicit cloud prediction. The cloud state is defined as 3D spatially distributed hydrometeors characterized with microphysical properties: mixing ratio, number concentration, and size distribution. The Geostationary Operational Environmental Satellite-9 (GOES-9) imager visible and infrared measurements were used in a new four-dimensional variational data assimilation (4DVAR) mesoscale algorithm for a warm continental stratus cloud system case to test the impact of these observations on the cloud simulation. The new data assimilation algorithm includes the Regional Atmospheric Modeling System (RAMS) with explicit cloud state prediction, the associated adjoint system, and an observational operator for forward and adjoint integrations of the GOES radiances. The results show positive impact of GOES imager measurements on the 3D cloud short-term simulation during and after the assimilation. The impact was achieved through sensitivity of the radiances to the cloud droplet mixing ratio at observation time and a 4D correlation between the cloud and atmospheric thermal and dynamical environment in the forecast model. The dynamical response to the radiance observations was through enhanced large mesoscale vertical mixing while horizontal advection was weak in the case of stable continental stratus evolution.
Although the current experiments show measurable positive impact of the cloudy radiance measurements on the stratus cloud simulation, they clearly suggest the need to further address the problem of negative cloud cover forecast errors. These errors were only weakly corrected in the current study because of the small sensitivity of the visible and infrared window radiances to the cloud-free atmosphere.
Abstract
An evolving coastal observatory has been hosted by the National Oceanography Centre at Liverpool, United Kingdom, for more than nine years. Within this observatory an instrumented ferry system has been developed and operated to provide near-surface scientific measurements of the Irish Sea. Passenger vessels such as ferries have the potential to be used as cost-effective platforms for gathering high-resolution regular measurements of the properties of near-surface water along their routes. They are able to operate on an almost year-round basis, and they usually have a high tolerance to adverse weather conditions. Examples of the application of instrumented ferry systems include environmental monitoring, the generation of long-term measurement time series, the provision of information for predictive model validation, and data for model assimilation purposes.
This paper discusses the development of an engineering system installed on board an Irish Sea passenger ferry. Particular attention is paid to explaining the engineering development required to achieve a robust, automated measuring system that is suitable for long-term continuous operation. The ferry, operating daily between Birkenhead and Belfast or Dublin, United Kingdom, was instrumented between December 2003 and January 2011 when the route was closed. Measurements were recorded at a nominal interval of 100 m and real-time data were transmitted every 15 min. The quality of the data was assessed. The spatial and temporal variability of the temperature and salinity fields are investigated as the ferry crosses a variety of shelf sea and coastal water column types.
Abstract
An evolving coastal observatory has been hosted by the National Oceanography Centre at Liverpool, United Kingdom, for more than nine years. Within this observatory an instrumented ferry system has been developed and operated to provide near-surface scientific measurements of the Irish Sea. Passenger vessels such as ferries have the potential to be used as cost-effective platforms for gathering high-resolution regular measurements of the properties of near-surface water along their routes. They are able to operate on an almost year-round basis, and they usually have a high tolerance to adverse weather conditions. Examples of the application of instrumented ferry systems include environmental monitoring, the generation of long-term measurement time series, the provision of information for predictive model validation, and data for model assimilation purposes.
This paper discusses the development of an engineering system installed on board an Irish Sea passenger ferry. Particular attention is paid to explaining the engineering development required to achieve a robust, automated measuring system that is suitable for long-term continuous operation. The ferry, operating daily between Birkenhead and Belfast or Dublin, United Kingdom, was instrumented between December 2003 and January 2011 when the route was closed. Measurements were recorded at a nominal interval of 100 m and real-time data were transmitted every 15 min. The quality of the data was assessed. The spatial and temporal variability of the temperature and salinity fields are investigated as the ferry crosses a variety of shelf sea and coastal water column types.
Abstract
There is significant interannual variability in the atmospheric concentration of carbon dioxide (CO2) even when the effect of anthropogenic sources has been accounted for. This variability is well correlated with the El Niño–Southern Oscillation (ENSO) cycle. This behavior of the natural carbon cycle provides a valuable mechanism for validating carbon cycle models. The model in turn is a valuable tool for examining the processes involved in the relationship between ENSO and the carbon cycle.
A GCM coupled climate–carbon cycle model is used to study the mechanisms involved. The model simulates the observed temperature, precipitation, and CO2 response of the climate to the ENSO cycle. Climatic changes over land during El Niño events lead to decreased gross primary productivity and increased plant and soil respiration, and hence the terrestrial biosphere becomes a source of CO2 to the atmosphere. Conversely, during El Niño events, the ocean becomes a sink of CO2 because of reduction of equatorial Pacific outgassing as a result of decreased upwelling of carbon-rich deep water. During La Niña events the opposite occurs; the land becomes a sink and the ocean a source of CO2.
The magnitude of the model's response is such that the terrestrial biosphere releases about 1.8 GtC yr−1 for an El Niño with a Niño-3 index of magnitude 1 °C, and the oceans take up about 0.5 GtC yr−1. (1 GtC = 1015 g of carbon). The net global response is thus an increase in atmospheric CO2 of about 0.6 ppmv yr−1. This is in close agreement with the sensitivity of the observed CO2 record to ENSO events.
Abstract
There is significant interannual variability in the atmospheric concentration of carbon dioxide (CO2) even when the effect of anthropogenic sources has been accounted for. This variability is well correlated with the El Niño–Southern Oscillation (ENSO) cycle. This behavior of the natural carbon cycle provides a valuable mechanism for validating carbon cycle models. The model in turn is a valuable tool for examining the processes involved in the relationship between ENSO and the carbon cycle.
A GCM coupled climate–carbon cycle model is used to study the mechanisms involved. The model simulates the observed temperature, precipitation, and CO2 response of the climate to the ENSO cycle. Climatic changes over land during El Niño events lead to decreased gross primary productivity and increased plant and soil respiration, and hence the terrestrial biosphere becomes a source of CO2 to the atmosphere. Conversely, during El Niño events, the ocean becomes a sink of CO2 because of reduction of equatorial Pacific outgassing as a result of decreased upwelling of carbon-rich deep water. During La Niña events the opposite occurs; the land becomes a sink and the ocean a source of CO2.
The magnitude of the model's response is such that the terrestrial biosphere releases about 1.8 GtC yr−1 for an El Niño with a Niño-3 index of magnitude 1 °C, and the oceans take up about 0.5 GtC yr−1. (1 GtC = 1015 g of carbon). The net global response is thus an increase in atmospheric CO2 of about 0.6 ppmv yr−1. This is in close agreement with the sensitivity of the observed CO2 record to ENSO events.
Abstract
Kinematic models predict that a coherent structure, such as a jet or an eddy, in an unsteady flow can exchange fluid with its surroundings. The authors consider the significance of this effect for a fully nonlinear, dynamically consistent, barotropic model of a meandering jet. The calculated volume transport associated with this fluid exchange is comparable to that of fluid crossing the Gulf Stream through the detachment of rings. Although the model is barotropic and idealized in other ways, the transport calculations suggest that this exchange mechanism may be important in lateral transport or potential vorticity budget analyses for the Gulf Stream and other oceanic jets. The numerically simulated meandering jet is obtained by allowing a small-amplitude unstable meander to grow until a saturated state occurs. The resulting flow is characterized by finite-amplitude meanders propagating with nearly constant speed, and the results clearly illustrate the stretching and stirring of fluid particles along the edges of the recirculation regions south of the meander crests and north of the troughs. The fluid exchange and resulting transport across boundaries separating regions of predominantly prograde, retrograde, and recirculating motion is quantified using a dynamical systems analysis. The geometrical structures that result from the analysis are shown to be closely correlated with regions of the flow that are susceptible to high potential vorticity dissipation. Moreover, in a related study this analysis has been used to effectively predict the entrainment and detrainment of particles to and from the jet.
Abstract
Kinematic models predict that a coherent structure, such as a jet or an eddy, in an unsteady flow can exchange fluid with its surroundings. The authors consider the significance of this effect for a fully nonlinear, dynamically consistent, barotropic model of a meandering jet. The calculated volume transport associated with this fluid exchange is comparable to that of fluid crossing the Gulf Stream through the detachment of rings. Although the model is barotropic and idealized in other ways, the transport calculations suggest that this exchange mechanism may be important in lateral transport or potential vorticity budget analyses for the Gulf Stream and other oceanic jets. The numerically simulated meandering jet is obtained by allowing a small-amplitude unstable meander to grow until a saturated state occurs. The resulting flow is characterized by finite-amplitude meanders propagating with nearly constant speed, and the results clearly illustrate the stretching and stirring of fluid particles along the edges of the recirculation regions south of the meander crests and north of the troughs. The fluid exchange and resulting transport across boundaries separating regions of predominantly prograde, retrograde, and recirculating motion is quantified using a dynamical systems analysis. The geometrical structures that result from the analysis are shown to be closely correlated with regions of the flow that are susceptible to high potential vorticity dissipation. Moreover, in a related study this analysis has been used to effectively predict the entrainment and detrainment of particles to and from the jet.
Abstract
Due to lack of high spatial and temporal resolution boundary layer (BL) observations, the rapid changes in the near-storm environment are not well represented in current convective-scale numerical models. Better representation of the near-storm environment in model initial conditions will likely further improve the forecasts of severe convective weather. This study investigates the impact of assimilating high temporal resolution BL retrievals from two ground-based remote sensing instruments for short-term forecasts of a tornadic supercell event on 13 July 2015 during the Plains Elevated Convection At Night field campaign. The instruments are the Atmospheric Emitted Radiance Interferometer (AERI) that retrieves thermodynamic profiles and the Doppler lidar (DL) that measures horizontal wind profiles. Six sets of convective-scale ensemble data assimilation (DA) experiments are performed: two control experiments that assimilate conventional and WSR-88D radar observations using either relaxation-to-prior-spread (RTPS) or the adaptive inflation (AI) technique and four experiments similar to the control but that assimilate either DL or AERI or both observations in addition to all other observations that are in the control experiments. Results indicate a positive impact of AERI and DL observations in forecasting convective initiation (CI) and early evolution of the supercell storm. The experiment that employs the AI technique to assimilate BL observations in DA enhances the humidity in the near-storm environment and low-level convergence, which in turn helps forecasting CI. The forecast improvement is most pronounced during the first ~3 h. Results also indicate that the AERI observations have a larger impact compared to DL in predicting CI.
Abstract
Due to lack of high spatial and temporal resolution boundary layer (BL) observations, the rapid changes in the near-storm environment are not well represented in current convective-scale numerical models. Better representation of the near-storm environment in model initial conditions will likely further improve the forecasts of severe convective weather. This study investigates the impact of assimilating high temporal resolution BL retrievals from two ground-based remote sensing instruments for short-term forecasts of a tornadic supercell event on 13 July 2015 during the Plains Elevated Convection At Night field campaign. The instruments are the Atmospheric Emitted Radiance Interferometer (AERI) that retrieves thermodynamic profiles and the Doppler lidar (DL) that measures horizontal wind profiles. Six sets of convective-scale ensemble data assimilation (DA) experiments are performed: two control experiments that assimilate conventional and WSR-88D radar observations using either relaxation-to-prior-spread (RTPS) or the adaptive inflation (AI) technique and four experiments similar to the control but that assimilate either DL or AERI or both observations in addition to all other observations that are in the control experiments. Results indicate a positive impact of AERI and DL observations in forecasting convective initiation (CI) and early evolution of the supercell storm. The experiment that employs the AI technique to assimilate BL observations in DA enhances the humidity in the near-storm environment and low-level convergence, which in turn helps forecasting CI. The forecast improvement is most pronounced during the first ~3 h. Results also indicate that the AERI observations have a larger impact compared to DL in predicting CI.
