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- Author or Editor: John J. Kennedy x
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Abstract
The impact of including comprehensive estimates of observational uncertainties on a detection and attribution analysis of twentieth-century near-surface temperature variations is investigated. The error model of HadCRUT4, a dataset of land near-surface air temperatures and sea surface temperatures, provides estimates of measurement, sampling, and bias adjustment uncertainties. These uncertainties are incorporated into an optimal detection analysis that regresses simulated large-scale temporal and spatial variations in near-surface temperatures, driven by well-mixed greenhouse gas variations and other anthropogenic and natural factors, against observed changes. The inclusion of bias adjustment uncertainties increases the variance of the regression scaling factors and the range of attributed warming from well-mixed greenhouse gases by less than 20%. Including estimates of measurement and sampling errors has a much smaller impact on the results. The range of attributable greenhouse gas warming is larger across analyses exploring dataset structural uncertainty. The impact of observational uncertainties on the detection analysis is found to be small compared to other sources of uncertainty, such as model variability and methodological choices, but it cannot be ruled out that on different spatial and temporal scales this source of uncertainty may be more important. The results support previous conclusions that there is a dominant anthropogenic greenhouse gas influence on twentieth-century near-surface temperature increases.
Abstract
The impact of including comprehensive estimates of observational uncertainties on a detection and attribution analysis of twentieth-century near-surface temperature variations is investigated. The error model of HadCRUT4, a dataset of land near-surface air temperatures and sea surface temperatures, provides estimates of measurement, sampling, and bias adjustment uncertainties. These uncertainties are incorporated into an optimal detection analysis that regresses simulated large-scale temporal and spatial variations in near-surface temperatures, driven by well-mixed greenhouse gas variations and other anthropogenic and natural factors, against observed changes. The inclusion of bias adjustment uncertainties increases the variance of the regression scaling factors and the range of attributed warming from well-mixed greenhouse gases by less than 20%. Including estimates of measurement and sampling errors has a much smaller impact on the results. The range of attributable greenhouse gas warming is larger across analyses exploring dataset structural uncertainty. The impact of observational uncertainties on the detection analysis is found to be small compared to other sources of uncertainty, such as model variability and methodological choices, but it cannot be ruled out that on different spatial and temporal scales this source of uncertainty may be more important. The results support previous conclusions that there is a dominant anthropogenic greenhouse gas influence on twentieth-century near-surface temperature increases.
Abstract
Global-mean surface temperature is affected by both natural variability and anthropogenic forcing. This study is concerned with identifying and removing from global-mean temperatures the signatures of natural climate variability over the period January 1900–March 2009. A series of simple, physically based methodologies are developed and applied to isolate the climate impacts of three known sources of natural variability: the El Niño–Southern Oscillation (ENSO), variations in the advection of marine air masses over the high-latitude continents during winter, and aerosols injected into the stratosphere by explosive volcanic eruptions. After the effects of ENSO and high-latitude temperature advection are removed from the global-mean temperature record, the signatures of volcanic eruptions and changes in instrumentation become more clearly apparent. After the volcanic eruptions are subsequently filtered from the record, the residual time series reveals a nearly monotonic global warming pattern since ∼1950. The results also reveal coupling between the land and ocean areas on the interannual time scale that transcends the effects of ENSO and volcanic eruptions. Globally averaged land and ocean temperatures are most strongly correlated when ocean leads land by ∼2–3 months. These coupled fluctuations exhibit a complicated spatial signature with largest-amplitude sea surface temperature perturbations over the Atlantic Ocean.
Abstract
Global-mean surface temperature is affected by both natural variability and anthropogenic forcing. This study is concerned with identifying and removing from global-mean temperatures the signatures of natural climate variability over the period January 1900–March 2009. A series of simple, physically based methodologies are developed and applied to isolate the climate impacts of three known sources of natural variability: the El Niño–Southern Oscillation (ENSO), variations in the advection of marine air masses over the high-latitude continents during winter, and aerosols injected into the stratosphere by explosive volcanic eruptions. After the effects of ENSO and high-latitude temperature advection are removed from the global-mean temperature record, the signatures of volcanic eruptions and changes in instrumentation become more clearly apparent. After the volcanic eruptions are subsequently filtered from the record, the residual time series reveals a nearly monotonic global warming pattern since ∼1950. The results also reveal coupling between the land and ocean areas on the interannual time scale that transcends the effects of ENSO and volcanic eruptions. Globally averaged land and ocean temperatures are most strongly correlated when ocean leads land by ∼2–3 months. These coupled fluctuations exhibit a complicated spatial signature with largest-amplitude sea surface temperature perturbations over the Atlantic Ocean.
Abstract
A new analysis of sea surface temperature (SST) observations indicates notable uncertainty in observed decadal climate variability in the second half of the twentieth century, particularly during the decades following World War II. The uncertainties are revealed by exploring SST data binned separately for the two predominant measurement types: “engine-room intake” (ERI) and “bucket” measurements. ERI measurements indicate large decreases in global-mean SSTs from 1950 to 1975, whereas bucket measurements indicate increases in SST over this period before bias adjustments are applied but decreases after they are applied. The trends in the bias adjustments applied to the bucket data are larger than the global-mean trends during the period 1950–75, and thus the global-mean trends during this period derive largely from the adjustments themselves. This is critical, since the adjustments are based on incomplete information about the underlying measurement methods and are thus subject to considerable uncertainty. The uncertainty in decadal-scale variability is particularly pronounced over the North Pacific, where the sign of low-frequency variability through the 1950s to 1970s is different for each measurement type. The uncertainty highlighted here has important—but in our view widely overlooked—implications for the interpretation of observed decadal climate variability over both the Pacific and Atlantic basins during the mid-to-late twentieth century.
Abstract
A new analysis of sea surface temperature (SST) observations indicates notable uncertainty in observed decadal climate variability in the second half of the twentieth century, particularly during the decades following World War II. The uncertainties are revealed by exploring SST data binned separately for the two predominant measurement types: “engine-room intake” (ERI) and “bucket” measurements. ERI measurements indicate large decreases in global-mean SSTs from 1950 to 1975, whereas bucket measurements indicate increases in SST over this period before bias adjustments are applied but decreases after they are applied. The trends in the bias adjustments applied to the bucket data are larger than the global-mean trends during the period 1950–75, and thus the global-mean trends during this period derive largely from the adjustments themselves. This is critical, since the adjustments are based on incomplete information about the underlying measurement methods and are thus subject to considerable uncertainty. The uncertainty in decadal-scale variability is particularly pronounced over the North Pacific, where the sign of low-frequency variability through the 1950s to 1970s is different for each measurement type. The uncertainty highlighted here has important—but in our view widely overlooked—implications for the interpretation of observed decadal climate variability over both the Pacific and Atlantic basins during the mid-to-late twentieth century.
Abstract
Over the past decade, numerous field campaigns and laboratory experiments have examined air–sea momentum exchange in the deep ocean. These studies have changed the understanding of drag coefficient behavior in hurricane force winds, with a general consensus that a limiting value is reached. Near the shore, wave conditions are markedly different than in deep water because of wave shoaling and breaking processes, but only very limited data exist to assess drag coefficient behavior. Yet, knowledge of the wind stress in this region is critical for storm surge forecasting, evaluating the low-level wind field across the coastal transition zone, and informing the wind load standard along the hurricane-prone coastline. During Hurricane Ike (2008), a Texas Tech University StickNet platform obtained wind measurements in marine exposure with a fetch across the Houston ship channel. These data were used to estimate drag coefficient dependence on wind speed. Wave conditions in the ship channel and surge level at the StickNet location were simulated using the Simulating Waves Nearshore Model coupled to the Advanced Circulation Model. The simulated waves were indicative of a fetch-limited condition with maximum significant wave heights reaching 1.5 m and peak periods of 4 s. A maximum surge depth of 0.6 m inundated the StickNet. Similar to deep water studies, findings indicate that the drag coefficient reaches a limiting value at wind speeds near hurricane force. However, at wind speeds below hurricane force, the drag coefficient is higher than that of deep water datasets, particularly at the slowest wind speeds.
Abstract
Over the past decade, numerous field campaigns and laboratory experiments have examined air–sea momentum exchange in the deep ocean. These studies have changed the understanding of drag coefficient behavior in hurricane force winds, with a general consensus that a limiting value is reached. Near the shore, wave conditions are markedly different than in deep water because of wave shoaling and breaking processes, but only very limited data exist to assess drag coefficient behavior. Yet, knowledge of the wind stress in this region is critical for storm surge forecasting, evaluating the low-level wind field across the coastal transition zone, and informing the wind load standard along the hurricane-prone coastline. During Hurricane Ike (2008), a Texas Tech University StickNet platform obtained wind measurements in marine exposure with a fetch across the Houston ship channel. These data were used to estimate drag coefficient dependence on wind speed. Wave conditions in the ship channel and surge level at the StickNet location were simulated using the Simulating Waves Nearshore Model coupled to the Advanced Circulation Model. The simulated waves were indicative of a fetch-limited condition with maximum significant wave heights reaching 1.5 m and peak periods of 4 s. A maximum surge depth of 0.6 m inundated the StickNet. Similar to deep water studies, findings indicate that the drag coefficient reaches a limiting value at wind speeds near hurricane force. However, at wind speeds below hurricane force, the drag coefficient is higher than that of deep water datasets, particularly at the slowest wind speeds.
No abstract available.
No abstract available.
Abstract
Global surface temperature changes are a fundamental expression of climate change. Recent, much-debated variations in the observed rate of surface temperature change have highlighted the importance of uncertainty in adjustments applied to sea surface temperature (SST) measurements. These adjustments are applied to compensate for systematic biases and changes in observing protocol. Better quantification of the adjustments and their uncertainties would increase confidence in estimated surface temperature change and provide higher-quality gridded SST fields for use in many applications.
Bias adjustments have been based on either physical models of the observing processes or the assumption of an unchanging relationship between SST and a reference dataset, such as night marine air temperature. These approaches produce similar estimates of SST bias on the largest space and time scales, but regional differences can exceed the estimated uncertainty. We describe challenges to improving our understanding of SST biases. Overcoming these will require clarification of past observational methods, improved modeling of biases associated with each observing method, and the development of statistical bias estimates that are less sensitive to the absence of metadata regarding the observing method.
New approaches are required that embed bias models, specific to each type of observation, within a robust statistical framework. Mobile platforms and rapid changes in observation type require biases to be assessed for individual historic and present-day platforms (i.e., ships or buoys) or groups of platforms. Lack of observational metadata and high-quality observations for validation and bias model development are likely to remain major challenges.
Abstract
Global surface temperature changes are a fundamental expression of climate change. Recent, much-debated variations in the observed rate of surface temperature change have highlighted the importance of uncertainty in adjustments applied to sea surface temperature (SST) measurements. These adjustments are applied to compensate for systematic biases and changes in observing protocol. Better quantification of the adjustments and their uncertainties would increase confidence in estimated surface temperature change and provide higher-quality gridded SST fields for use in many applications.
Bias adjustments have been based on either physical models of the observing processes or the assumption of an unchanging relationship between SST and a reference dataset, such as night marine air temperature. These approaches produce similar estimates of SST bias on the largest space and time scales, but regional differences can exceed the estimated uncertainty. We describe challenges to improving our understanding of SST biases. Overcoming these will require clarification of past observational methods, improved modeling of biases associated with each observing method, and the development of statistical bias estimates that are less sensitive to the absence of metadata regarding the observing method.
New approaches are required that embed bias models, specific to each type of observation, within a robust statistical framework. Mobile platforms and rapid changes in observation type require biases to be assessed for individual historic and present-day platforms (i.e., ships or buoys) or groups of platforms. Lack of observational metadata and high-quality observations for validation and bias model development are likely to remain major challenges.
Abstract
Day-to-day variations in surface air temperature affect society in many ways, but daily surface air temperature measurements are not available everywhere. Therefore, a global daily picture cannot be achieved with measurements made in situ alone and needs to incorporate estimates from satellite retrievals. This article presents the science developed in the EU Horizon 2020–funded EUSTACE project (2015–19, www.eustaceproject.org) to produce global and European multidecadal ensembles of daily analyses of surface air temperature complementary to those from dynamical reanalyses, integrating different ground-based and satellite-borne data types. Relationships between surface air temperature measurements and satellite-based estimates of surface skin temperature over all surfaces of Earth (land, ocean, ice, and lakes) are quantified. Information contained in the satellite retrievals then helps to estimate air temperature and create global fields in the past, using statistical models of how surface air temperature varies in a connected way from place to place; this needs efficient statistical analysis methods to cope with the considerable data volumes. Daily fields are presented as ensembles to enable propagation of uncertainties through applications. Estimated temperatures and their uncertainties are evaluated against independent measurements and other surface temperature datasets. Achievements in the EUSTACE project have also included fundamental preparatory work useful to others, for example, gathering user requirements, identifying inhomogeneities in daily surface air temperature measurement series from weather stations, carefully quantifying uncertainties in satellite skin and air temperature estimates, exploring the interaction between air temperature and lakes, developing statistical models relevant to non-Gaussian variables, and methods for efficient computation.
Abstract
Day-to-day variations in surface air temperature affect society in many ways, but daily surface air temperature measurements are not available everywhere. Therefore, a global daily picture cannot be achieved with measurements made in situ alone and needs to incorporate estimates from satellite retrievals. This article presents the science developed in the EU Horizon 2020–funded EUSTACE project (2015–19, www.eustaceproject.org) to produce global and European multidecadal ensembles of daily analyses of surface air temperature complementary to those from dynamical reanalyses, integrating different ground-based and satellite-borne data types. Relationships between surface air temperature measurements and satellite-based estimates of surface skin temperature over all surfaces of Earth (land, ocean, ice, and lakes) are quantified. Information contained in the satellite retrievals then helps to estimate air temperature and create global fields in the past, using statistical models of how surface air temperature varies in a connected way from place to place; this needs efficient statistical analysis methods to cope with the considerable data volumes. Daily fields are presented as ensembles to enable propagation of uncertainties through applications. Estimated temperatures and their uncertainties are evaluated against independent measurements and other surface temperature datasets. Achievements in the EUSTACE project have also included fundamental preparatory work useful to others, for example, gathering user requirements, identifying inhomogeneities in daily surface air temperature measurement series from weather stations, carefully quantifying uncertainties in satellite skin and air temperature estimates, exploring the interaction between air temperature and lakes, developing statistical models relevant to non-Gaussian variables, and methods for efficient computation.
