The Circumpolar Active Layer Monitoring (CALM) network is an ongoing international effort to collect and disseminate standardized measurements of active-layer dynamics to monitor the response of near-surface permafrost parameters to climate change. This work presents a distillation of 25 years (1995-2019) of observations from three north-south transects of CALM sites in tundra environments of Alaska. Transects examined in this work bisect tundra regions of discontinuous permafrost on the Seward Peninsula, and the continuous permafrost zone on the western and eastern sections of the Arctic Foothills and Arctic Coastal Plain. These transects represent regional climatic gradients, several physiographic provinces, and regionally characteristic landcover associations. Total active-layer thickening at observed sites ranged from 7 to 26 cm; more significant thaw occurred in the foothills despite less pronounced warming air temperature trends. This summary highlights several regional active layer responses to climate warming, complicated by distinct thermal landscape sensitivities, landscape variability, and documented thaw subsidence. Data summarized in this report are publicly available and represent an important validation resource for earth-system models that include regions in the continuous and discontinuous permafrost zones of northern and western Alaska.

To better understand the ecological and hydrological responses to climatic and cryospheric changes, the spatiotemporal variations in the active layer thickness (ALT) need to be scrupulously studied. Based on more than 230 sites from the circumpolar active layer monitoring network, the spatiotemporal characteristics of the ALT across the northern hemisphere during 1990-2015 were investigated. Results indicate that the ALT exhibits substantial spatial variations across the northern hemisphere, ranging from approximately 30 cm in the arctic and subarctic regions to greater than 10 m in the mountainous permafrost regions at mid-latitudes. Regional averages of ALT are 48 cm in Alaska, 93 cm in Canada, 164 cm in the Nordic countries (including Greenland and Svalbard) and Switzerland, 330 cm in Mongolia, 476 cm in Kazakhstan, and 230 cm on the Qinghai-Tibetan Plateau (QTP), respectively. In Russia, the regional averages of ALT in European North, West Siberia, Central Siberia, Northeast Siberia, Chukotka, and Kamchatka are 110, 92, 69, 61, 53 and 60 cm, respectively. Increasing trends of ALT were not uniformly present in the observational records. Significant changes in the ALT were observed at 73 sites, approximately 43.2 % of the investigated 169 sites that are available for statistical analysis. Less than 25 % Alaskan sites and approximately 33 % Canadian sites showed significant increase in the ALT. On the QTP, almost all the sites showed significant ALT increases. Insignificant increase and even decrease in the ALT were observed in some parts of the northern hemisphere, e.g., Mongolia, parts of Alaska and Canada. The air and ground temperatures, vegetation, substrate, microreliefs, and soil moisture in particular, play decisive roles in the spatiotemporal variations in the ALT, but the relationships among each other are complicated and await further studies.

The Arctic is experiencing an unprecedented rate of environmental and climate change. The active layer ( the uppermost layer of soil between the atmosphere and permafrost that freezes in winter and thaws in summer) is sensitive to both climatic and environmental changes, and plays an important role in the functioning, planning, and economic activities of Arctic human and natural ecosystems. This study develops a methodology for modeling and estimating spatial-temporal variations in active layer thickness ( ALT) using data from several sites of the Circumpolar Active Layer Monitoring network, and demonstrates its use in spatial-temporal interpolation. The simplest model's stochastic component exhibits no spatial or spatio-temporal dependency and is referred to as the naive model, against which we evaluate the performance of the other models, which assume that the stochastic component exhibits either spatial or spatio-temporal dependency. The methods used to fit the models are then discussed, along with point forecasting. We compare the predicted fit of the various models at key study sites located in the North Slope of Alaska and demonstrate the advantages of space-time models through a series of error statistics such as mean squared error, mean absolute and percent deviance from observed data. We find the difference in performance between the spatio-temporal and remaining models is significant for all three error statistics. The best stochastic spatio-temporal model increases predictive accuracy, compared to the naive model, of 33.3%, 36.2% and 32.5% on average across the three error metrics at the key sites for a one-year hold out period.