Periodic Reporting for period 5 - SPACE (Space-time structure of climate change)
Período documentado: 2023-09-01 hasta 2024-02-29
In order to understand and predict the human influence on climate, it is particularly important to quantify the structure of internal climate variability and the sensitivity of the climate system to external forcing. Empirical evidence and physics suggest an intrinsic link between time scale and the associated spatial scale of climate variations.: While fast variations such as weather are regional, glacial-interglacial cycles appear to be globally coherent. SPACE quantifies this presumed tendency of the climate system to reduce its degrees of freedom on longer time scales and uses it to constrain the sparse, noisy and at times contradictory evidence of past climate changes and to inform us about the amplitude, origins and mechanisms of climate variability.
By systematically analyzing instrumental and paleo-records, we 1) determine the space-time structure of climate changes on annual to millennial time scales. This provides the prerequisite for mapping past climate changes and allow to confront climate models with robust estimates of climate variability across spatial scales; 2) provide a clearer separation of internal and external forced climate variability, by leveraging their distinct space-time structures; 3) examine the past relationship of state and variability to learn how climate variability might change in a warmer future.
By systematically analyzing instrumental and paleo-records, we 1) determine the space-time structure of climate changes on annual to millennial time scales. This provides the prerequisite for mapping past climate changes and allow to confront climate models with robust estimates of climate variability across spatial scales; 2) provide a clearer separation of internal and external forced climate variability, by leveraging their distinct space-time structures; 3) examine the past relationship of state and variability to learn how climate variability might change in a warmer future.
We established the statistical basis to estimate the space-time structure of climate variability from instrumental and paleo-data. We analyzed a set of replicate sediment cores, purchased the planned equipment, and published multiple research papers. We assembled a team of several postdocs, PhD students, and student helpers to cover research components in all work packages. Additionally, we gave several invited talks at international conferences and seminars and organized workshops on climate variability. Furthermore, we developed new methods to estimate the time-scale-dependent spatial structure and applied them to climate model and instrumental data. We analyzed replicate proxy records cores to better distinguish local from large-scale climate variability and characterize the time-uncertainty in proxy records. Since the estimated signal-to-noise ratios from some proxy types were low, we further enhanced our understanding of and refined new proxies for climate variability reconstructions. This endeavor included developing methods to reconstruct fast climate variability from sediment cores (Mass Spectral Imaging), gaining an in-depth comprehension of the climate signal recorded in ice cores, and developing methods to overcome the high noise level and uncertainties in the transfer function. To utilize our understanding for interpreting and correcting climate variability estimates, we implemented this knowledge in proxy system models and developed a theoretical framework to handle time-scale-dependent proxy uncertainties.
These advancements provide a foundation for estimating the space-time structure from annual to millennial time-scales by combining instrumental observations and various paleo-climate archives. We reconstructed local and global temperature variability, demonstrating that regional climate variations persisted for longer time scales than climate models simulating past climate states are able to reproduce. This suggests an underestimation of regional variability on multidecadal and longer time scales, as well as a bias in climate projections and attribution studies. We proposed that one reason for this discrepancy is the likely underestimation of marine temperature variability in model simulations, which drives the centennial-millennial variability on land. We conducted the first quantitative reconstruction of changes in temperature variability between the Last Glacial Maximum and the Holocene based on a global network of marine and terrestrial temperature proxies in order to study the mean state dependency of climate variability. Our findings revealed that the overall pattern of reduced variability could be explained by changes in the meridional temperature gradient, a mechanism that may indicate further decreases in temperature variability in a warmer future. Lastly, we estimated the signal-to-noise ratio of Holocene and Glacial temperature reconstructions. Due to the relatively low estimated signal-to-noise ratios, we decided to develop and optimize a method to reconstruct fast climate variability from sediment cores (Individual Foraminifera Analysis). This approach will enable us to refine our estimates of how climate variability may change in a warmer world.
These advancements provide a foundation for estimating the space-time structure from annual to millennial time-scales by combining instrumental observations and various paleo-climate archives. We reconstructed local and global temperature variability, demonstrating that regional climate variations persisted for longer time scales than climate models simulating past climate states are able to reproduce. This suggests an underestimation of regional variability on multidecadal and longer time scales, as well as a bias in climate projections and attribution studies. We proposed that one reason for this discrepancy is the likely underestimation of marine temperature variability in model simulations, which drives the centennial-millennial variability on land. We conducted the first quantitative reconstruction of changes in temperature variability between the Last Glacial Maximum and the Holocene based on a global network of marine and terrestrial temperature proxies in order to study the mean state dependency of climate variability. Our findings revealed that the overall pattern of reduced variability could be explained by changes in the meridional temperature gradient, a mechanism that may indicate further decreases in temperature variability in a warmer future. Lastly, we estimated the signal-to-noise ratio of Holocene and Glacial temperature reconstructions. Due to the relatively low estimated signal-to-noise ratios, we decided to develop and optimize a method to reconstruct fast climate variability from sediment cores (Individual Foraminifera Analysis). This approach will enable us to refine our estimates of how climate variability may change in a warmer world.
We derived the first time-scale dependent estimators for spatial degrees of freedom that allow us to estimate the space-time structure of gridded datasets and extended this
method to be applicable to incomplete and sparser paleo-datasets. We characterized the information content of Holocene and Glacial compilations of temperature proxies and provided the first systematic characterization of the information contained in Individual Foraminifera Data, a new proxy to estimate climate variability from sediment records. We developed methods to separate signal and noise from empirical data (‘Spectral ANOVA’) as well as predicting the time-scale dependent uncertainty in proxy data from a mechanistic understanding of the proxy recording process (‘Proxy Spectral Error Model’). We developed innovative methods to characterize key parameters of this proxy recording process. This includes the estimation of sedimentary mixing and heterogeneity using replicated radiocarbon dates and a characterization of the formation of the ice-core signals using photogrammetry and snow trenches. All these advances set the basis to systematically characterize and use the space-time structure of climate variability for a better understanding of the climate system.
We separated the spatio-temporal structure of internal variability and forced variability in climate model simulations, showing that at least in the model world, both internal and forced variability are well separated in their spatio-temporal pattern. We achieved the first quantitative reconstruction of changes in temperature variability between the Last Glacial Maximum and the Holocene showing that warmer climates might be associated with less temperature variability related to the reduced latitudinal temperature gradients. Our first spatial terrestrial temperature variability reconstruction suggests that long term natural variability on land is driven by ocean variability and that an underestimation of ocean variability might cause the discrepancy between current climate model simulations and proxy records. We found a good agreement of the overall spatio-temporal structure of surface temperature in models and instrumental observations but strong evidence that on longer time scales (multi-decadal and longer), temperature variations stay more localized than currently simulated by climate models. Our results have implications for benchmarking and improving the representation of variability in climate models and the quantification of future regional climate risks linked to natural variability. They are followed up by different research initiatives, for example the third phase of the PAGES working group Climate Variability Across Scales. Our work on overcoming the low signal content in high resolution water isotope data prompted an POC ERC application to commercialize a borehole thermometry device.
method to be applicable to incomplete and sparser paleo-datasets. We characterized the information content of Holocene and Glacial compilations of temperature proxies and provided the first systematic characterization of the information contained in Individual Foraminifera Data, a new proxy to estimate climate variability from sediment records. We developed methods to separate signal and noise from empirical data (‘Spectral ANOVA’) as well as predicting the time-scale dependent uncertainty in proxy data from a mechanistic understanding of the proxy recording process (‘Proxy Spectral Error Model’). We developed innovative methods to characterize key parameters of this proxy recording process. This includes the estimation of sedimentary mixing and heterogeneity using replicated radiocarbon dates and a characterization of the formation of the ice-core signals using photogrammetry and snow trenches. All these advances set the basis to systematically characterize and use the space-time structure of climate variability for a better understanding of the climate system.
We separated the spatio-temporal structure of internal variability and forced variability in climate model simulations, showing that at least in the model world, both internal and forced variability are well separated in their spatio-temporal pattern. We achieved the first quantitative reconstruction of changes in temperature variability between the Last Glacial Maximum and the Holocene showing that warmer climates might be associated with less temperature variability related to the reduced latitudinal temperature gradients. Our first spatial terrestrial temperature variability reconstruction suggests that long term natural variability on land is driven by ocean variability and that an underestimation of ocean variability might cause the discrepancy between current climate model simulations and proxy records. We found a good agreement of the overall spatio-temporal structure of surface temperature in models and instrumental observations but strong evidence that on longer time scales (multi-decadal and longer), temperature variations stay more localized than currently simulated by climate models. Our results have implications for benchmarking and improving the representation of variability in climate models and the quantification of future regional climate risks linked to natural variability. They are followed up by different research initiatives, for example the third phase of the PAGES working group Climate Variability Across Scales. Our work on overcoming the low signal content in high resolution water isotope data prompted an POC ERC application to commercialize a borehole thermometry device.