ALBERT

Description: Ice-wedge polygons are common features of lowland tundra in the continuous permafrost zone and prone to rapid degradation through melting of ground ice. There are many inter-related processes involved in ice-wedge thermokarst and it is a major challenge to quantify their influence on the stability of the permafrost underlying the landscape. In this study we used a numerical modelling approach to investigate the degradation of ice-wedges with a focus on the influence of hydrological conditions. Our study area was Samoylov Island in the Lena River delta of Northern Siberia, for which we had in-situ measurements to evaluate the model. The tailored version of the CryoGrid3 Land Surface Model was capable of simulating the changing micro-topography of polygonal tundra and also regarded lateral fluxes of heat, water, and snow. We demonstrated that the approach is capable of simulating ice-wedge degradation and the associated transition from a low-centred to a high-centred polygonal micro-topography. The model simulations showed ice-wedge degradation under recent climatic conditions of the study area, irrespective of hydrological conditions. However, we found that wetter conditions lead to an earlier onset of degradation and cause more rapid ground subsidence. We set our findings in correspondence to observed types of ice-wedge polygons in the study area and hypothesized on remaining discrepancies between modelled and observed ice-wedge thermokarst activity. Our quantitative approach provides a valuable complement to previous, more qualitative and conceptual, descriptions of the possible pathways of ice-wedge polygon evolution. We concluded that our study is a blueprint for investigating thermokarst landforms and marks a step forward in understanding the complex interrelationships between various processes shaping ice-rich permafrost landscapes.

Description: Most of the world's permafrost is located in the Arctic, where its frozen organic carbon con-tent makes it a potentially important influence on the global climate system. The Arctic climate appears to be changing more rapidly than the lower latitudes, but observational data density in the region is low. Permafrost thaw and carbon release into the atmosphere is a positive feed-back mechanism that has the potential for climate warming. It is therefore particularly im-portant to understand the links between the energy balance, which can vary rapidly over hour-ly to annual time scales, and permafrost condition, which changes slowly on decadal to cen-tennial timescales. This requires long-term observational data such as that available from the Samoylov research site in northern Siberia, where meteorological parameters, energy balance, and subsurface observations have been recorded since 1998. This paper presents the temporal data set produced between 2002 and 2017, explaining the instrumentation, calibration, pro-cessing and data quality control. Additional data include a high-resolution digital terrain mod-el (DTM) obtained from terrestrial LiDAR laser scanning. Since the data provide observations of temporally variable parameters that influence energy fluxes between permafrost, active lay-er soils, and the atmosphere (such as snow depth and soil moisture content), they are suitable for calibrating and quantifying the dynamics of permafrost as a component in earth system models. The data also include soil properties beneath different microtopographic features (a polygon center, a rim, a slope, and a trough), yielding much-needed information on landscape heterogeneity for use in land surface modeling. For the record from 1998 to 2017, the average mean annual air temperature was −12.3°C, with mean monthly temperature of the warmest month (July) recorded as 9.5°C and for the coldest month (February) −32.7°C. The average annual rainfall was 169mm. The depth of zero annual amplitude niveau is at 20.8m, and has warmed from −9.1°C in 2006 to −7.7°C in 2017. The presented data are available in the supplementary material of this paper and through the PANGAEA website (https://doi.org/10.1594/PANGAEA.891142).

Description: Earth system models (ESMs) are our primary tool for projecting future climate change, but their ability to represent small-scale land surface processes is currently limited. This is especially true for permafrost landscapes in which melting of excess ground ice and subsequent subsidence affect lateral processes which can substantially alter soil conditions and fluxes of heat, water, and carbon to the atmosphere. Here we demonstrate that dynamically changing microtopography and related lateral fluxes of snow, water, and heat can be represented through a tiling approach suitable for implementation in large-scale models, and we investigate which of these lateral processes are important to reproduce observed landscape evolution. Combining existing methods for representing excess ground ice, snow redistribution, and lateral water and energy fluxes in two coupled tiles, we show that the model approach can simulate observed degradation processes in two very different permafrost landscapes. We are able to simulate the transition from low-centered to high-centered polygons, when applied to polygonal tundra in the cold, continuous permafrost zone, which results in (i) a more realistic representation of soil conditions through drying of elevated features and wetting of lowered features with related changes in energy fluxes, (ii) up to 2 ∘C reduced average permafrost temperatures in the current (2000–2009) climate, (iii) delayed permafrost degradation in the future RCP4.5 scenario by several decades, and (iv) more rapid degradation through snow and soil water feedback mechanisms once subsidence starts. Applied to peat plateaus in the sporadic permafrost zone, the same two-tile system can represent an elevated peat plateau underlain by permafrost in a surrounding permafrost-free fen and its degradation in the future following a moderate warming scenario. These results demonstrate the importance of representing lateral fluxes to realistically simulate both the current permafrost state and its degradation trajectories as the climate continues to warm. Implementing laterally coupled tiles in ESMs could improve the representation of a range of permafrost processes, which is likely to impact the simulated magnitude and timing of the permafrost–carbon feedback.

Description: Earth System Models (ESMs) are our primary tool for projecting future climate change, but are currently limited in their ability to represent small-scale land-surface processes. This is especially the case for permafrost landscapes, where melting of excess ground ice and subsequent subsidence affect lateral processes which can substantially alter soil conditions and fluxes of heat, water and carbon to the atmosphere. Here we demonstrate how dynamically changing microtopography and related lateral fluxes of snow, water and heat can be represented with a tiling approach suitable for implementation in large-scale models, and investigate which of these lateral processes are important to reproduce observed landscape evolution. Combining existing methods for representing excess ground ice, snow redistribution and lateral water and energy fluxes in two coupled tiles, we show how the same model approach can simulate known degradation processes in two very different kinds of permafrost landscapes. Applied to polygonal tundra in the cold, continuous permafrost zone, we are able to simulate the transition from low-centered to high-centered polygons, and show how this results in i) more realistic representation of soil conditions through drying of elevated features and wetting of lowered features with related changes in energy fluxes, ii) reduced average permafrost temperatures at 13m depth with up to 2°C in current (2000–2009) climate, iii) delayed permafrost degradation in the future RCP4.5 scenario by several decades, and iv) more rapid degradation through snow and soil water feedback mechanisms once subsidence starts. Applied to warm, sporadic permafrost features, this two-tile system can represent an elevated peat plateau underlain by permafrost in a surrounding permafrost-free fen, and how it degrades in the future following a moderate warming scenario. These results show the importance of representing lateral fluxes to realistically simulate both the current permafrost state and its degradation trajectories as the climate continues to warm; both of which are likely to have important implications for simulations of the magnitude and timing of the permafrost carbon feedback.

Description: Most of the world's permafrost is located in the Arctic, where its frozen organic carbon content makes it a potentially important influence on the global climate system. The Arctic climate appears to be changing more rapidly than the lower latitudes, but observational data density in the region is low. Permafrost thaw and carbon release into the atmosphere, as well as snow cover changes, are positive feedback mechanisms that have the potential for climate warming. It is therefore particularly important to understand the links between the energy balance, which can vary rapidly over hourly to annual timescales, and permafrost conditions, which changes slowly on decadal to centennial timescales. This requires long-term observational data such as that available from the Samoylov research site in northern Siberia, where meteorological parameters, energy balance, and subsurface observations have been recorded since 1998. This paper presents the temporal data set produced between 2002 and 2017, explaining the instrumentation, calibration, processing, and data quality control. Furthermore, we present a merged data set of the parameters, which were measured from 1998 onwards. Additional data include a high-resolution digital terrain model (DTM) obtained from terrestrial lidar laser scanning. Since the data provide observations of temporally variable parameters that influence energy fluxes between permafrost, active-layer soils, and the atmosphere (such as snow depth and soil moisture content), they are suitable for calibrating and quantifying the dynamics of permafrost as a component in earth system models. The data also include soil properties beneath different microtopographic features (a polygon centre, a rim, a slope, and a trough), yielding much-needed information on landscape heterogeneity for use in land surface modelling. For the record from 1998 to 2017, the average mean annual air temperature was −12.3 ∘C, with mean monthly temperature of the warmest month (July) recorded as 9.5 ∘C and for the coldest month (February) −32.7 ∘C. The average annual rainfall was 169 mm. The depth of zero annual amplitude is at 20.75 m. At this depth, the temperature has increased from −9.1 ∘C in 2006 to −7.7 ∘C in 2017. The presented data are freely available through the PANGAEA (https://doi.org/10.1594/PANGAEA.891142) and Zenodo (https://zenodo.org/record/2223709, last access: 6 February 2019) websites.

Description: Ice-wedge polygons are common features of lowland tundra in the continuous permafrost zone and prone to rapid degradation through melting of ground ice. There are many interrelated processes involved in ice-wedge thermokarst and it is a major challenge to quantify their influence on the stability of the permafrost underlying the landscape. In this study we used a numerical modelling approach to investigate the degradation of ice wedges with a focus on the influence of hydrological conditions. Our study area was Samoylov Island in the Lena River delta of northern Siberia, for which we had in situ measurements to evaluate the model. The tailored version of the CryoGrid 3 land surface model was capable of simulating the changing microtopography of polygonal tundra and also regarded lateral fluxes of heat, water, and snow. We demonstrated that the approach is capable of simulating ice-wedge degradation and the associated transition from a low-centred to a high-centred polygonal microtopography. The model simulations showed ice-wedge degradation under recent climatic conditions of the study area, irrespective of hydrological conditions. However, we found that wetter conditions lead to an earlier onset of degradation and cause more rapid ground subsidence. We set our findings in correspondence to observed types of ice-wedge polygons in the study area and hypothesized on remaining discrepancies between modelled and observed ice-wedge thermokarst activity. Our quantitative approach provides a valuable complement to previous, more qualitative and conceptual, descriptions of the possible pathways of ice-wedge polygon evolution. We concluded that our study is a blueprint for investigating thermokarst landforms and marks a step forward in understanding the complex interrelationships between various processes shaping ice-rich permafrost landscapes.

Description: In continuous permafrost lowlands, thawing of ice-rich deposits and melting of massive ground ice lead to abrupt landscape changes called thermokarst, which have widespread consequences on the thermal, hydrological, and biogeochemical state of the subsurface. However, macro-scale land surface models (LSMs) do not resolve such localized subgrid-scale processes and could hence miss key feedback mechanisms and complexities which affect permafrost degradation and the potential liberation of soil organic carbon in high latitudes. Here, we extend the CryoGrid 3 permafrost model with a multi-scale tiling scheme which represents the spatial heterogeneities of surface and subsurface conditions in ice-rich permafrost lowlands. We conducted numerical simulations using stylized model setups to assess how different representations of micro- and meso-scale heterogeneities affect landscape evolution pathways and the amount of permafrost degradation in response to climate warming. At the micro-scale, the terrain was assumed to be either homogeneous or composed of ice-wedge polygons, and at the meso-scale it was assumed to be either homogeneous or resembling a low-gradient slope. We found that by using different model setups and parameter sets, a multitude of landscape evolution pathways could be simulated which correspond well to observed thermokarst landscape dynamics across the Arctic. These pathways include the formation, growth, and gradual drainage of thaw lakes; the transition from low-centred to high-centred ice-wedge polygons; and the formation of landscape-wide drainage systems due to melting of ice wedges. Moreover, we identified several feedback mechanisms due to lateral transport processes which either stabilize or destabilize the thermokarst terrain. The amount of permafrost degradation in response to climate warming was found to depend primarily on the prevailing hydrological conditions, which in turn are crucially affected by whether or not micro- and/or meso-scale heterogeneities were considered in the model setup. Our results suggest that the multi-scale tiling scheme allows for simulating ice-rich permafrost landscape dynamics in a more realistic way than simplistic one-dimensional models and thus facilitates more robust assessments of permafrost degradation pathways in response to climate warming. Our modelling work improves the understanding of how micro- and meso-scale processes affect the evolution of ice-rich permafrost landscapes, and it informs macro-scale modellers focusing on high-latitude land surface processes about the necessities and possibilities for the inclusion of subgrid-scale processes such as thermokarst within their models.

Description: Total column ozone (TCO) data from multiple satellite-based instruments have been combined to create a single near-global daily time series of ozone fields at 1.25∘ longitude by 1∘ latitude spanning the period 31 October 1978 to 31 December 2016. Comparisons against TCO measurements from the ground-based Dobson and Brewer spectrophotometer networks are used to remove offsets and drifts between the ground-based measurements and a subset of the satellite-based measurements. The corrected subset is then used as a basis for homogenizing the remaining data sets. The construction of this database improves on earlier versions of the database maintained first by the National Institute of Water and Atmospheric Research (NIWA) and now by Bodeker Scientific (BS), referred to as the NIWA-BS TCO database. The intention is for the NIWA-BS TCO database to serve as a climate data record for TCO, and to this end, the requirements for constructing climate data records, as detailed by GCOS (the Global Climate Observing System), have been followed as closely as possible. This new version includes a wider range of satellite-based instruments, uses updated sources of satellite data, extends the period covered, uses improved statistical methods to model the difference fields when homogenizing the data sets, and, perhaps most importantly, robustly tracks uncertainties from the source data sets through to the final climate data record which is now accompanied by associated uncertainty fields. Furthermore, a gap-free TCO database (referred to as the BS-filled TCO database) has been created and is documented in this paper. The utility of the NIWA-BS TCO database is demonstrated through an analysis of ozone trends from November 1978 to December 2016. Both databases are freely available for non-commercial purposes: the DOI for the NIWA-BS TCO database is https://doi.org/10.5281/zenodo.1346424 (Bodeker et al., 2018) and is available from https://zenodo.org/record/1346424. The DOI for the BS-filled TCO database is https://doi.org/10.5281/zenodo.3908787 (Bodeker et al., 2020) and is available from https://zenodo.org/record/3908787. In addition, both data sets are available from http://www.bodekerscientific.com/data/total-column-ozone (last access: June 2021).