Effective societal responses to rapid climate change in the Arctic rely on an accurate representation of region-specific ecosystem properties and processes. However, this is limited by the scarcity and patchy distribution of field measurements. Here, we use a comprehensive, geo-referenced database of primary field measurements in 1,840 published studies across the Arctic to identify statistically significant spatial biases in field sampling and study citation across this globally important region. We find that 31% of all study citations are derived from sites located within 50 km of just two research sites: Toolik Lake in the USA and Abisko in Sweden. Furthermore, relatively colder, more rapidly warming and sparsely vegetated sites are under-sampled and under-recognized in terms of citations, particularly among microbiology-related studies. The poorly sampled and cited areas, mainly in the Canadian high-Arctic archipelago and the Arctic coastline of Russia, constitute a large fraction of the Arctic ice-free land area. Our results suggest that the current pattern of sampling and citation may bias the scientific consensuses that underpin attempts to accurately predict and effectively mitigate climate change in the region. Further work is required to increase both the quality and quantity of sampling, and incorporate existing literature from poorly cited areas to generate a more representative picture of Arctic climate change and its environmental impacts.

The majority of northern peatlands were initiated during the Holocene. Owing to their mass imbalance, they have sequestered huge amounts of carbon in terrestrial ecosystems. Although recent syntheses have filled some knowledge gaps, the extent and remoteness of many peatlands pose challenges to developing reliable regional carbon accumulation estimates from observations. In this work, we employed an individual‐ and patch‐based dynamic global vegetation model (LPJ‐GUESS) with peatland and permafrost functionality to quantify long‐term carbon accumulation rates in northern peatlands and to assess the effects of historical and projected future climate change on peatland carbon balance. We combined published datasets of peat basal age to form an up‐to‐date peat inception surface for the pan‐Arctic region which we then used to constrain the model. We divided our analysis into two parts, with a focus both on the carbon accumulation changes detected within the observed peatland boundary and at pan‐Arctic scale under two contrasting warming scenarios (representative concentration pathway—RCP8.5 and RCP2.6). We found that peatlands continue to act as carbon sinks under both warming scenarios, but their sink capacity will be substantially reduced under the high‐warming (RCP8.5) scenario after 2050. Areas where peat production was initially hampered by permafrost and low productivity were found to accumulate more carbon because of the initial warming and moisture‐rich environment due to permafrost thaw, higher precipitation and elevated CO2 levels. On the other hand, we project that areas which will experience reduced precipitation rates and those without permafrost will lose more carbon in the near future, particularly peatlands located in the European region and between 45 and 55°N latitude. Overall, we found that rapid global warming could reduce the carbon sink capacity of the northern peatlands in the coming decades.

Modeling soil thermal dynamics at high latitudes and altitudes requires representations of physical processes such as snow insulation, soil freezing and thawing and subsurface conditions like soil water/ice content and soil texture. We have compared six different land models: JSBACH, ORCHIDEE, JULES, COUP, HYBRID8 and LPJ-GUESS, at four different sites with distinct cold region landscape types, to identify the importance of physical processes in capturing observed temperature dynamics in soils. The sites include alpine, high Arctic, wet polygonal tundra and non-permafrost Arctic, thus showing how a range of models can represent distinct soil temperature regimes. For all sites, snow insulation is of major importance for estimating topsoil conditions. However, soil physics is essential for the subsoil temperature dynamics and thus the active layer thicknesses. This analysis shows that land models need more realistic surface processes, such as detailed snow dynamics and moss cover with changing thickness and wetness, along with better representations of subsoil thermal dynamics

Abstract. The CryoGrid community model is a flexible toolbox for simulating the ground thermal regime and the ice/water balance for permafrost and glaciers, extending a well-established suite of permafrost models (CryoGrid 1, 2 and 3). The CryoGrid community model can accommodate a wide variety of application scenarios, which is achieved by fully modular structures through object-oriented programming. Different model components, characterized by their process representations and parametrizations, are realized as classes (i.e. objects) in CryoGrid. Standardized communication protocols between these classes ensure that they can be stacked vertically. For example, the CryoGrid community model features several classes with different complexity for the seasonal snow cover which can be flexibly combined with a range of classes representing subsurface materials, each with their own set of process representations (e.g. soil with and without water balance, glacier ice). We present the CryoGrid architecture as well as the model physics and defining equations for the different model classes, focusing on one-dimensional model configurations which can also interact with external heat and water reservoirs. We illustrate the wide variety of simulation capabilities for a site on Svalbard, with point-scale permafrost simulations using e.g. different soil freezing characteristics, drainage regimes and snow representations, as well as simulations for glacier mass balance and a shallow water body. The CryoGrid community model is not intended as a static model framework, but aims to provide developers with a flexible platform for efficient model development. In this study, we document both basic and advanced model functionalities to provide a baseline for the future development of novel cryosphere models.

Abstract. Most northern peatlands developed during the Holocene, sequestering large amounts of carbon in terrestrial ecosystems. However, recent syntheses have highlighted the gaps in our understanding of peatland carbon accumulation. Assessments of the long-term carbon accumulation rate and possible warming-driven changes in these accumulation rates can therefore benefit from process-based modelling studies. We employed an individual-based dynamic global ecosystem model with dynamic peatland and permafrost functionalities and patch-based vegetation dynamics to quantify long-term carbon accumulation rates and to assess the effects of historical and projected climate change on peatland carbon balances across the pan-Arctic region. Our results are broadly consistent with published regional and global carbon accumulation estimates. A majority of modelled peatland sites in Scandinavia, Europe, Russia and central and eastern Canada change from carbon sinks through the Holocene to potential carbon sources in the coming century. In contrast, the carbon sink capacity of modelled sites in Siberia, far eastern Russia, Alaska and western and northern Canada was predicted to increase in the coming century. The greatest changes were evident in eastern Siberia, north-western Canada and in Alaska, where peat production hampered by permafrost and low productivity due the cold climate in these regions in the past was simulated to increase greatly due to warming, a wetter climate and higher CO 2 levels by the year 2100. In contrast, our model predicts that sites that are expected to experience reduced precipitation rates and are currently permafrost free will lose more carbon in the future.