ALBERT

Description: Holocene temperature proxy records are commonly used in quantitative synthesis and model-data comparisons. However, comparing correlations between time series from records collected in proximity to one another with the expected correlations based on climate model simulations indicates either regional or noisy climate signals in Holocene temperature proxy records. In this study, we evaluate the consistency of spatial correlations present in Holocene proxy records with those found in data from the Last Glacial Maximum (LGM). Specifically, we predict correlations expected in LGM proxy records if the only difference to Holocene correlations would be due to more time uncertainty and more climate variability in the LGM. We compare this simple prediction to the actual correlation structure in the LGM proxy records. We found that time series data of ice-core stable isotope records and planktonic foraminifera Mg/Ca ratios were consistent between the Holocene and LGM periods, while time series of Uk'37 proxy records were not as we found no correlation between nearby LGM records. Our results support the finding of highly regional or noisy marine proxy records in the compilation analysed here and suggest the need for further studies on the role of climate proxies and the processes of climate signal recording and preservation.

Description: Key Points: - Novel micro-analytical techniques allow seasonally resolved climate proxy data from varved marine sediments - Potential to generate seasonal and inter annual resolution sea surface temperature proxy time series spanning 〉1,000 years - Thorough assessment of processes that influence the climate signal recovered from proxies, validated with careful replication, is required Three recently published papers including Napier et al. (2022, https://doi.org/10.1029/2021PA004355) utilize novel microanalytical approaches with varved marine sediments to demonstrate the potential to reconstruct seasonal and inter-annual climate variability. Obtaining paleoclimate data at a resolution akin to the observational record is vitally important for improving our understanding of climate phenomena such as monsoons and modes of variability such as the El Niño Southern Oscillation, for which appraisals of past inter-annual variability is critical. The ability to generate seasonal and inter annual resolution sea surface temperature proxy time series spanning a thousand years or more is revolutionary and has the potential to fill gaps in our knowledge of climate variability. Although generally limited to sediments from regions with oxygen depleted bottom waters, there is great potential to integrate shorter seasonal resolution climate “snap shots” from other archives such as annually banded corals into composite time series. But as paleoceanographic data are used more by the observational and modeling fields, we make the case for conducting a thorough case-by-case assessment of the processes that influence the climate signal recovered from proxies, using careful replication to validate new approaches. Understanding or exploring the potential influence of processes which effectively filter the climate signal will lead to more quantitative paleoceanographic data that will better serve the broader climate science community.

Description: The density of firn is an important property for monitoring and modeling the ice sheet as well as to model the pore close-off and thus to interpret ice core-based greenhouse gas records. One feature, which is still in debate, is the potential existence of an annual cycle of firn density in low-accumulation regions. Several studies describe or assume seasonally successive density layers, horizontally evenly distributed, as seen in radar data. On the other hand, high-resolution density measurements on firn cores in Antarctica and Greenland showed no clear seasonal cycle in the top few meters. A major caveat of most existing snow-pit and firn-core based studies is that they represent one vertical profile from a laterally heterogeneous density field. To overcome this, we created an extensive dataset of horizontal and vertical density data at Kohnen Station, Dronning Maud Land on the East Antarctic Plateau. We drilled and analyzed three 90 m long firn cores as well as 160 one meter long vertical profiles from two elongated snow trenches to obtain a two dimensional view of the density variations. The analysis of the 45 m wide and 1 m deep density fields reveals a seasonal cycle in density. However, the seasonality is overprinted by strong stratigraphic noise, making it invisible when analyzing single firn cores. Our density dataset extends the view from the local ice-core perspective to a hundred meter scale and thus supports linking spatially integrating methods such as radar and seismic studies to ice and firn cores.

Description: Global climatic changes expected in the next centuries are likely to cause unparalleled vegetation disturbances, which in turn impact ecosystem services. To assess the significance of disturbances, it is necessary to characterize and understand typical natural vegetation variability on multi-decadal timescales and longer. We investigate this in the Holocene vegetation by examining a taxonomically harmonized and temporally standardized global fossil pollen dataset. Using principal component analysis, we characterize the variability in pollen assemblages, which are a proxy for vegetation composition, and derive timescale-dependent estimates of variability using the first-order Haar structure function. We find, on average, increasing fluctuations in vegetation composition from centennial to millennial timescales, as well as spatially coherent patterns of variability. We further relate these variations to pairwise comparisons between biome classes based on vegetation composition. As such, higher variability is identified for open-land vegetation compared to forests. This is consistent with the more active fire regimes of open-land biomes fostering variability. Needleleaf forests are more variable than broadleaf forests on shorter (centennial) timescales, but the inverse is true on longer (millennial) timescales. This inversion could also be explained by the fire characteristics of the biomes as fire disturbances would increase vegetation variability on shorter timescales, but stabilize vegetation composition on longer timecales by preventing the migration of less fire-adapted species.