Climate Variability and Extremes during the Past 100 Years, 2008
... Oceanic Technol., 23, 464–475, doi:10.1175/JTECH1843.1. Kent, EC, DI Berry, SD Woodruff, and ... more ... Oceanic Technol., 23, 464–475, doi:10.1175/JTECH1843.1. Kent, EC, DI Berry, SD Woodruff, and PK Taylor, 2006: Voluntary observing ships: a ... Parker, D., E. Kent, S. Woodruff, D. Dehenauw, DE Harrison, T. Manabe, M. Mietus, V. Swail, and S. Worley, 2004: Second JCOMM ...
... Kingdom. Email: [email protected]. 1. Introduction. Observations ... error. Th... more ... Kingdom. Email: [email protected]. 1. Introduction. Observations ... error. The sample in the bucket can cool by evaporation from the top surface or from the walls of the bucket in dry or windy conditions (Parker 1985). If ...
... Kingdom. Email: [email protected]. 1. Introduction. Observations ... error. Th... more ... Kingdom. Email: [email protected]. 1. Introduction. Observations ... error. The sample in the bucket can cool by evaporation from the top surface or from the walls of the bucket in dry or windy conditions (Parker 1985). If ...
Potential extensions to each of the ocean observing system"s mobile platform networks, made possi... more Potential extensions to each of the ocean observing system"s mobile platform networks, made possible by new technologies, are examined with respect to the value of the complete observing system. The autonomous instrument networks now have the potential for the truly global scope that will come from extensions to high latitudes, into marginal seas and the deep ocean, and by high-resolution sampling in boundary currents. The autonomous networks can accommodate new sensors, including oxygen, chlorophyll-A, and particulate organic carbon, and coordination with shipboard and moored platform programs will enable studies of the impacts of climate variability and change on biogeochemistry and ecosystems. The systems required to observe ocean surface properties, surface circulation, and air-sea exchanges merit further study since improvements in these areas will come not only from new instrumentation but through better coordination between networks and better use of research and commercial vessels. The observing system infrastructure must evolve in parallel with the system"s scope and complexity. Expanded roles are seen for smaller research vessels, including instrument deployment and recovery, reference-quality profile measurements and underway surface observations. The data management system must provide the rigorous control needed for the production of research quality datasets. The challenge in providing these enhancements to the ocean observing system is to define and achieve an optimal mix of shipboard and autonomous observations to deliver climate-quality datasets in a cost-efficient manner that exploits the synergies between satellite and in situ observations.
... (1994) as test studies indicated that it provides a better definition of frontal features, fo... more ... (1994) as test studies indicated that it provides a better definition of frontal features, for example, the Gulf Stream, in well-sampled areas. Climatologically ice-covered regions were excluded from the analysis using the ice mask of Alexander and Mobley (1976). ...
Abstract Results from an analysis of the Southampton Oceanography Centre (SOC) global wind stress... more Abstract Results from an analysis of the Southampton Oceanography Centre (SOC) global wind stress climatology, which is based on in situ reports for the period 1980-93, are presented. The accuracy of the SOC stresses has been assessed at several locations by ...
ABSTRACT A new record of sea surface temperature (SST) for climate applications is described. Thi... more ABSTRACT A new record of sea surface temperature (SST) for climate applications is described. This record provides independent corroboration of global variations estimated from SST measurements made in situ. Infrared imagery from Along-Track Scanning Radiometers (ATSRs) is used to create a 20 year time series of SST at 0.1° latitude-longitude resolution, in the ATSR Reprocessing for Climate (ARC) project. A very high degree of independence of in situ measurements is achieved via physics-based techniques. Skin SST and SST estimated for 20 cm depth are provided, with grid cell uncertainty estimates. Comparison with in situ data sets establishes that ARC SSTs generally have bias of order 0.1 K or smaller. The precision of the ARC SSTs is 0.14 K during 2003 to 2009, from three-way error analysis. Over the period 1994 to 2010, ARC SSTs are stable, with better than 95% confidence, to within 0.005 K yr-1(demonstrated for tropical regions). The data set appears useful for cleanly quantifying interannual variability in SST and major SST anomalies. The ARC SST global anomaly time series is compared to the in situ-based Hadley Centre SST data set version 3 (HadSST3). Within known uncertainties in bias adjustments applied to in situ measurements, the independent ARC record and HadSST3 present the same variations in global marine temperature since 1996. Since the in situ observing system evolved significantly in its mix of measurement platforms and techniques over this period, ARC SSTs provide an important corroboration that HadSST3 accurately represents recent variability and change in this essential climate variable.
1] We present the Met Office Hadley Centre's sea ice and sea surface temperature (SST) data set, ... more 1] We present the Met Office Hadley Centre's sea ice and sea surface temperature (SST) data set, HadISST1, and the nighttime marine air temperature (NMAT) data set, HadMAT1. HadISST1 replaces the global sea ice and sea surface temperature (GISST) data sets and is a unique combination of monthly globally complete fields of SST and sea ice concentration on a 1°latitude-longitude grid from 1871. The companion HadMAT1 runs monthly from 1856 on a 5°latitude-longitude grid and incorporates new corrections for the effect on NMAT of increasing deck (and hence measurement) heights. HadISST1 and HadMAT1 temperatures are reconstructed using a two-stage reducedspace optimal interpolation procedure, followed by superposition of quality-improved gridded observations onto the reconstructions to restore local detail. The sea ice fields are made more homogeneous by compensating satellite microwave-based sea ice concentrations for the impact of surface melt effects on retrievals in the Arctic and for algorithm deficiencies in the Antarctic and by making the historical in situ concentrations consistent with the satellite data. SSTs near sea ice are estimated using statistical relationships between SST and sea ice concentration. HadISST1 compares well with other published analyses, capturing trends in global, hemispheric, and regional SST well, containing SST fields with more uniform variance through time and better month-tomonth persistence than those in GISST. HadMAT1 is more consistent with SST and with collocated land surface air temperatures than previous NMAT data sets. Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century,
... (1994) as test studies indicated that it provides a better definition of frontal features, fo... more ... (1994) as test studies indicated that it provides a better definition of frontal features, for example, the Gulf Stream, in well-sampled areas. Climatologically ice-covered regions were excluded from the analysis using the ice mask of Alexander and Mobley (1976). ...
Journal of Atmospheric and Oceanic Technology, 1999
The random observational errors for meteorological variables within the Comprehensive Ocean-Atmos... more The random observational errors for meteorological variables within the Comprehensive Ocean-Atmosphere Dataset (COADS) have been determined using the semivariogram statistical technique. The error variance has been calculated using four months of data, spanning summer and winter months and the start and end of the dataset. The random errors found range from 1.3 to 2.8 m s Ϫ1 for 10-m-corrected wind speed, 1.2 to 7.1 mb for surface pressure, 0.8Њ to 3.3ЊC for 10-m air temperature, 0.4Њ to 2.8ЊC for sea surface temperature, and 0.6 to 1.8 g kg Ϫ1 for 10-m specific humidity. The air temperature and specific humidity random observational errors contain a dependence on their mean values, but correlations between errors and mean values are low for the other variables analyzed. The accuracy of the error estimates increases with the number of observational data pairs used in the analysis. Wind speed random observational errors were reduced by height correction and by the use of the Lindau Beaufort Scale.
Journal of Atmospheric and Oceanic Technology, 2007
... However, a subsequent WMO report (Shearman and Zelenko 1989) recommended a method for reducin... more ... However, a subsequent WMO report (Shearman and Zelenko 1989) recommended a method for reducing the measured wind speed to a 10-m reference height “at the time of observation or soon after.” WMO policy has apparently been that a wind speed adjusted to 10-m height ...
Journal of Atmospheric and Oceanic Technology, 2006
... Kingdom. Email: [email protected]. 1. Introduction. Observations ... error. Th... more ... Kingdom. Email: [email protected]. 1. Introduction. Observations ... error. The sample in the bucket can cool by evaporation from the top surface or from the walls of the bucket in dry or windy conditions (Parker 1985). If ...
Marine winds reported by Voluntary Observing Ships (VOS) and moored buoys require adjustment to p... more Marine winds reported by Voluntary Observing Ships (VOS) and moored buoys require adjustment to provide a homogeneous record of the marine climate. Known sources of inhomogeneity arise from differences in measurement height and method, averaging method and atmospheric stability; methods are available to correct for these. However, significant differences remain in a paired dataset of ship and buoy winds. Regression methods to remove this remaining inconsistency are discussed, and a ranked regression method chosen as most appropriate to adjust ship wind speeds to yield a similar distribution. We show the factors, such as vessel type, that affect the regression results. The corrections, derived from a high-quality paired dataset with rigorous quality control, are effective at reducing inhomogeneity in monthly mean wind speed distributions derived from the International Comprehensive Ocean-Atmosphere Data Set.
Climate Variability and Extremes during the Past 100 Years, 2008
... Oceanic Technol., 23, 464–475, doi:10.1175/JTECH1843.1. Kent, EC, DI Berry, SD Woodruff, and ... more ... Oceanic Technol., 23, 464–475, doi:10.1175/JTECH1843.1. Kent, EC, DI Berry, SD Woodruff, and PK Taylor, 2006: Voluntary observing ships: a ... Parker, D., E. Kent, S. Woodruff, D. Dehenauw, DE Harrison, T. Manabe, M. Mietus, V. Swail, and S. Worley, 2004: Second JCOMM ...
... Kingdom. Email: [email protected]. 1. Introduction. Observations ... error. Th... more ... Kingdom. Email: [email protected]. 1. Introduction. Observations ... error. The sample in the bucket can cool by evaporation from the top surface or from the walls of the bucket in dry or windy conditions (Parker 1985). If ...
... Kingdom. Email: [email protected]. 1. Introduction. Observations ... error. Th... more ... Kingdom. Email: [email protected]. 1. Introduction. Observations ... error. The sample in the bucket can cool by evaporation from the top surface or from the walls of the bucket in dry or windy conditions (Parker 1985). If ...
Potential extensions to each of the ocean observing system"s mobile platform networks, made possi... more Potential extensions to each of the ocean observing system"s mobile platform networks, made possible by new technologies, are examined with respect to the value of the complete observing system. The autonomous instrument networks now have the potential for the truly global scope that will come from extensions to high latitudes, into marginal seas and the deep ocean, and by high-resolution sampling in boundary currents. The autonomous networks can accommodate new sensors, including oxygen, chlorophyll-A, and particulate organic carbon, and coordination with shipboard and moored platform programs will enable studies of the impacts of climate variability and change on biogeochemistry and ecosystems. The systems required to observe ocean surface properties, surface circulation, and air-sea exchanges merit further study since improvements in these areas will come not only from new instrumentation but through better coordination between networks and better use of research and commercial vessels. The observing system infrastructure must evolve in parallel with the system"s scope and complexity. Expanded roles are seen for smaller research vessels, including instrument deployment and recovery, reference-quality profile measurements and underway surface observations. The data management system must provide the rigorous control needed for the production of research quality datasets. The challenge in providing these enhancements to the ocean observing system is to define and achieve an optimal mix of shipboard and autonomous observations to deliver climate-quality datasets in a cost-efficient manner that exploits the synergies between satellite and in situ observations.
... (1994) as test studies indicated that it provides a better definition of frontal features, fo... more ... (1994) as test studies indicated that it provides a better definition of frontal features, for example, the Gulf Stream, in well-sampled areas. Climatologically ice-covered regions were excluded from the analysis using the ice mask of Alexander and Mobley (1976). ...
Abstract Results from an analysis of the Southampton Oceanography Centre (SOC) global wind stress... more Abstract Results from an analysis of the Southampton Oceanography Centre (SOC) global wind stress climatology, which is based on in situ reports for the period 1980-93, are presented. The accuracy of the SOC stresses has been assessed at several locations by ...
ABSTRACT A new record of sea surface temperature (SST) for climate applications is described. Thi... more ABSTRACT A new record of sea surface temperature (SST) for climate applications is described. This record provides independent corroboration of global variations estimated from SST measurements made in situ. Infrared imagery from Along-Track Scanning Radiometers (ATSRs) is used to create a 20 year time series of SST at 0.1° latitude-longitude resolution, in the ATSR Reprocessing for Climate (ARC) project. A very high degree of independence of in situ measurements is achieved via physics-based techniques. Skin SST and SST estimated for 20 cm depth are provided, with grid cell uncertainty estimates. Comparison with in situ data sets establishes that ARC SSTs generally have bias of order 0.1 K or smaller. The precision of the ARC SSTs is 0.14 K during 2003 to 2009, from three-way error analysis. Over the period 1994 to 2010, ARC SSTs are stable, with better than 95% confidence, to within 0.005 K yr-1(demonstrated for tropical regions). The data set appears useful for cleanly quantifying interannual variability in SST and major SST anomalies. The ARC SST global anomaly time series is compared to the in situ-based Hadley Centre SST data set version 3 (HadSST3). Within known uncertainties in bias adjustments applied to in situ measurements, the independent ARC record and HadSST3 present the same variations in global marine temperature since 1996. Since the in situ observing system evolved significantly in its mix of measurement platforms and techniques over this period, ARC SSTs provide an important corroboration that HadSST3 accurately represents recent variability and change in this essential climate variable.
1] We present the Met Office Hadley Centre's sea ice and sea surface temperature (SST) data set, ... more 1] We present the Met Office Hadley Centre's sea ice and sea surface temperature (SST) data set, HadISST1, and the nighttime marine air temperature (NMAT) data set, HadMAT1. HadISST1 replaces the global sea ice and sea surface temperature (GISST) data sets and is a unique combination of monthly globally complete fields of SST and sea ice concentration on a 1°latitude-longitude grid from 1871. The companion HadMAT1 runs monthly from 1856 on a 5°latitude-longitude grid and incorporates new corrections for the effect on NMAT of increasing deck (and hence measurement) heights. HadISST1 and HadMAT1 temperatures are reconstructed using a two-stage reducedspace optimal interpolation procedure, followed by superposition of quality-improved gridded observations onto the reconstructions to restore local detail. The sea ice fields are made more homogeneous by compensating satellite microwave-based sea ice concentrations for the impact of surface melt effects on retrievals in the Arctic and for algorithm deficiencies in the Antarctic and by making the historical in situ concentrations consistent with the satellite data. SSTs near sea ice are estimated using statistical relationships between SST and sea ice concentration. HadISST1 compares well with other published analyses, capturing trends in global, hemispheric, and regional SST well, containing SST fields with more uniform variance through time and better month-tomonth persistence than those in GISST. HadMAT1 is more consistent with SST and with collocated land surface air temperatures than previous NMAT data sets. Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century,
... (1994) as test studies indicated that it provides a better definition of frontal features, fo... more ... (1994) as test studies indicated that it provides a better definition of frontal features, for example, the Gulf Stream, in well-sampled areas. Climatologically ice-covered regions were excluded from the analysis using the ice mask of Alexander and Mobley (1976). ...
Journal of Atmospheric and Oceanic Technology, 1999
The random observational errors for meteorological variables within the Comprehensive Ocean-Atmos... more The random observational errors for meteorological variables within the Comprehensive Ocean-Atmosphere Dataset (COADS) have been determined using the semivariogram statistical technique. The error variance has been calculated using four months of data, spanning summer and winter months and the start and end of the dataset. The random errors found range from 1.3 to 2.8 m s Ϫ1 for 10-m-corrected wind speed, 1.2 to 7.1 mb for surface pressure, 0.8Њ to 3.3ЊC for 10-m air temperature, 0.4Њ to 2.8ЊC for sea surface temperature, and 0.6 to 1.8 g kg Ϫ1 for 10-m specific humidity. The air temperature and specific humidity random observational errors contain a dependence on their mean values, but correlations between errors and mean values are low for the other variables analyzed. The accuracy of the error estimates increases with the number of observational data pairs used in the analysis. Wind speed random observational errors were reduced by height correction and by the use of the Lindau Beaufort Scale.
Journal of Atmospheric and Oceanic Technology, 2007
... However, a subsequent WMO report (Shearman and Zelenko 1989) recommended a method for reducin... more ... However, a subsequent WMO report (Shearman and Zelenko 1989) recommended a method for reducing the measured wind speed to a 10-m reference height “at the time of observation or soon after.” WMO policy has apparently been that a wind speed adjusted to 10-m height ...
Journal of Atmospheric and Oceanic Technology, 2006
... Kingdom. Email: [email protected]. 1. Introduction. Observations ... error. Th... more ... Kingdom. Email: [email protected]. 1. Introduction. Observations ... error. The sample in the bucket can cool by evaporation from the top surface or from the walls of the bucket in dry or windy conditions (Parker 1985). If ...
Marine winds reported by Voluntary Observing Ships (VOS) and moored buoys require adjustment to p... more Marine winds reported by Voluntary Observing Ships (VOS) and moored buoys require adjustment to provide a homogeneous record of the marine climate. Known sources of inhomogeneity arise from differences in measurement height and method, averaging method and atmospheric stability; methods are available to correct for these. However, significant differences remain in a paired dataset of ship and buoy winds. Regression methods to remove this remaining inconsistency are discussed, and a ranked regression method chosen as most appropriate to adjust ship wind speeds to yield a similar distribution. We show the factors, such as vessel type, that affect the regression results. The corrections, derived from a high-quality paired dataset with rigorous quality control, are effective at reducing inhomogeneity in monthly mean wind speed distributions derived from the International Comprehensive Ocean-Atmosphere Data Set.
Uploads
Papers by Elizabeth Kent