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- Author or Editor: Klaus Hasselmann x
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Abstract
The problem of extracting directional spectra from observed, multi-component wave data has two facets: 1) the observations provide information only on a finite number of integral properties of the wave field; hence the directional spectrum cannot be determined uniquely from the wave data alone; and 2) the observations contain statistical errors. These difficulties are dealt with by choosing an optimal directional spectrum model which simultaneously minimizes some integral property of the spectrum (its “nastiness”) and passes an appropriate test of statistical significance. Although developed here in the context of surface wave directional spectra, the technique (adopted from the Backus-Gilbert inverse method).is applicable to any problem requiring the fitting of a model to data which represent integral properties of the function being modeled.
Abstract
The problem of extracting directional spectra from observed, multi-component wave data has two facets: 1) the observations provide information only on a finite number of integral properties of the wave field; hence the directional spectrum cannot be determined uniquely from the wave data alone; and 2) the observations contain statistical errors. These difficulties are dealt with by choosing an optimal directional spectrum model which simultaneously minimizes some integral property of the spectrum (its “nastiness”) and passes an appropriate test of statistical significance. Although developed here in the context of surface wave directional spectra, the technique (adopted from the Backus-Gilbert inverse method).is applicable to any problem requiring the fitting of a model to data which represent integral properties of the function being modeled.
Abstract
The sensitivity of the global ocean circulation to changes in surface heat flux forcing is studied using the Hamburg Large Scale Geostrophic (LSG) ocean circulation model. The simulated mean ocean circulation for appropriately chosen surface forcing fields reproduces the principal water mass properties, residence times, and large-scale transport properties of the observed ocean circulation quite realistically within the constraints of the model resolution. However, rather minor changes in the formulation of the high-latitude air–sea heat flux can produce dramatic changes in the structure of the ocean circulation. These strongly affect the deep-ocean overturning rates and residence times, the oceanic heat transport, and the rate of oceanic uptake of CO2.
The sensitivity is largely controlled by the mechanism of deep-water formation in high latitudes. The experiments support similar findings by other authors on the sensitivity of the ocean circulation to changes in the fresh-water flux and are consistent with the existence of multiequilibria circulation states with a relatively low transition threshold.
Abstract
The sensitivity of the global ocean circulation to changes in surface heat flux forcing is studied using the Hamburg Large Scale Geostrophic (LSG) ocean circulation model. The simulated mean ocean circulation for appropriately chosen surface forcing fields reproduces the principal water mass properties, residence times, and large-scale transport properties of the observed ocean circulation quite realistically within the constraints of the model resolution. However, rather minor changes in the formulation of the high-latitude air–sea heat flux can produce dramatic changes in the structure of the ocean circulation. These strongly affect the deep-ocean overturning rates and residence times, the oceanic heat transport, and the rate of oceanic uptake of CO2.
The sensitivity is largely controlled by the mechanism of deep-water formation in high latitudes. The experiments support similar findings by other authors on the sensitivity of the ocean circulation to changes in the fresh-water flux and are consistent with the existence of multiequilibria circulation states with a relatively low transition threshold.
Abstract
A strategy using statistically optimal fingerprints to detect anthropogenic climate change is outlined and applied to near-surface temperature trends. The components of this strategy include observations, information about natural climate variability, and a “guess pattern” representing the expected time–space pattern of anthropogenic climate change. The expected anthropogenic climate change is identified through projection of the observations onto an appropriate optimal fingerprint, yielding a scalar-detection variable. The statistically optimal fingerprint is obtained by weighting the components of the guess pattern (truncated to some small-dimensional space) toward low-noise directions. The null hypothesis that the observed climate change is part of natural climate variability is then tested.
This strategy is applied to detecting a greenhouse-gas-induced climate change in the spatial pattern of near-surface temperature trends defined for time intervals of 15–30 years. The expected pattern of climate change is derived from a transient simulation with a coupled ocean-atmosphere general circulation model. Global gridded near-surface temperature observations are used to represent the observed climate change. Information on the natural variability needed to establish the statistics of the detection variable is extracted from long control simulations of coupled ocean-atmosphere models and, additionally, from the observations themselves (from which an estimated greenhouse warming signal has been removed). While the model control simulations contain only variability caused by the internal dynamics of the atmosphere-ocean system, the observations additionally contain the response to various external forcings (e.g., volcanic eruptions, changes in solar radiation, and residual anthropogenic forcing). The resulting estimate of climate noise has large uncertainties but is qualitatively the best the authors can presently offer.
The null hypothesis that the latest observed 20-yr and 30-yr trend of near-surface temperature (ending in 1994) is part of natural variability is rejected with a risk of less than 2.5% to 5% (the 5% level is derived from the variability of one model control simulation dominated by a questionable extreme event). In other words, the probability that the warming is due to our estimated natural variability is less than 2.5% to 5%. The increase in the signal-to-noise ratio by optimization of the fingerprint is of the order of 10%–30% in most cases.
The predicted signals are dominated by the global mean component; the pattern correlation excluding the global mean is positive but not very high. Both the evolution of the detection variable and also the pattern correlation results are consistent with the model prediction for greenhouse-gas-induced climate change. However, in order to attribute the observed warming uniquely to anthropogenic greenhouse gas forcing, more information on the climate's response to other forcing mechanisms (e.g., changes in solar radiation, volcanic, or anthropogenic sulfate aerosols) and their interaction is needed.
It is concluded that a statistically significant externally induced warming has been observed, but our caveat that the estimate of the internal climate variability is still uncertain is emphasized.
Abstract
A strategy using statistically optimal fingerprints to detect anthropogenic climate change is outlined and applied to near-surface temperature trends. The components of this strategy include observations, information about natural climate variability, and a “guess pattern” representing the expected time–space pattern of anthropogenic climate change. The expected anthropogenic climate change is identified through projection of the observations onto an appropriate optimal fingerprint, yielding a scalar-detection variable. The statistically optimal fingerprint is obtained by weighting the components of the guess pattern (truncated to some small-dimensional space) toward low-noise directions. The null hypothesis that the observed climate change is part of natural climate variability is then tested.
This strategy is applied to detecting a greenhouse-gas-induced climate change in the spatial pattern of near-surface temperature trends defined for time intervals of 15–30 years. The expected pattern of climate change is derived from a transient simulation with a coupled ocean-atmosphere general circulation model. Global gridded near-surface temperature observations are used to represent the observed climate change. Information on the natural variability needed to establish the statistics of the detection variable is extracted from long control simulations of coupled ocean-atmosphere models and, additionally, from the observations themselves (from which an estimated greenhouse warming signal has been removed). While the model control simulations contain only variability caused by the internal dynamics of the atmosphere-ocean system, the observations additionally contain the response to various external forcings (e.g., volcanic eruptions, changes in solar radiation, and residual anthropogenic forcing). The resulting estimate of climate noise has large uncertainties but is qualitatively the best the authors can presently offer.
The null hypothesis that the latest observed 20-yr and 30-yr trend of near-surface temperature (ending in 1994) is part of natural variability is rejected with a risk of less than 2.5% to 5% (the 5% level is derived from the variability of one model control simulation dominated by a questionable extreme event). In other words, the probability that the warming is due to our estimated natural variability is less than 2.5% to 5%. The increase in the signal-to-noise ratio by optimization of the fingerprint is of the order of 10%–30% in most cases.
The predicted signals are dominated by the global mean component; the pattern correlation excluding the global mean is positive but not very high. Both the evolution of the detection variable and also the pattern correlation results are consistent with the model prediction for greenhouse-gas-induced climate change. However, in order to attribute the observed warming uniquely to anthropogenic greenhouse gas forcing, more information on the climate's response to other forcing mechanisms (e.g., changes in solar radiation, volcanic, or anthropogenic sulfate aerosols) and their interaction is needed.
It is concluded that a statistically significant externally induced warming has been observed, but our caveat that the estimate of the internal climate variability is still uncertain is emphasized.
Abstract
This paper reviews recent research that assesses evidence for the detection of anthropogenic and natural external influences on the climate. Externally driven climate change has been detected by a number of investigators in independent data covering many parts of the climate system, including surface temperature on global and large regional scales, ocean heat content, atmospheric circulation, and variables of the free atmosphere, such as atmospheric temperature and tropopause height. The influence of external forcing is also clearly discernible in reconstructions of hemispheric-scale temperature of the last millennium. These observed climate changes are very unlikely to be due only to natural internal climate variability, and they are consistent with the responses to anthropogenic and natural external forcing of the climate system that are simulated with climate models. The evidence indicates that natural drivers such as solar variability and volcanic activity are at most partially responsible for the large-scale temperature changes observed over the past century, and that a large fraction of the warming over the last 50 yr can be attributed to greenhouse gas increases. Thus, the recent research supports and strengthens the IPCC Third Assessment Report conclusion that “most of the global warming over the past 50 years is likely due to the increase in greenhouse gases.”
Abstract
This paper reviews recent research that assesses evidence for the detection of anthropogenic and natural external influences on the climate. Externally driven climate change has been detected by a number of investigators in independent data covering many parts of the climate system, including surface temperature on global and large regional scales, ocean heat content, atmospheric circulation, and variables of the free atmosphere, such as atmospheric temperature and tropopause height. The influence of external forcing is also clearly discernible in reconstructions of hemispheric-scale temperature of the last millennium. These observed climate changes are very unlikely to be due only to natural internal climate variability, and they are consistent with the responses to anthropogenic and natural external forcing of the climate system that are simulated with climate models. The evidence indicates that natural drivers such as solar variability and volcanic activity are at most partially responsible for the large-scale temperature changes observed over the past century, and that a large fraction of the warming over the last 50 yr can be attributed to greenhouse gas increases. Thus, the recent research supports and strengthens the IPCC Third Assessment Report conclusion that “most of the global warming over the past 50 years is likely due to the increase in greenhouse gases.”
