Recent Results from the U.S. Geological Survey Permafrost and Climate Monitoring Network - North Slope Alaska
We have implemented the DOI/GTN-P (Department of Interior/Global Terrestrial Network - Permafrost) permafrost- and climate-monitoring network, spanning federal lands in arctic Alaska. The monitoring system serves both scientific and societal needs and currently consists of: a) a network of 17 automated ground-based stations which continuously monitor several GCOS (Global Climate Observing System) essential climate variables within permafrost and in the atmospheric boundary layer; b) a network of automated cameras which document rapid permafrost degradation, thermokarst development, coastal and lakeshore erosion, and bird habitats; and c) a 21-element deep borehole array which is used to monitor the thermal state of permafrost; this is the largest array of deep boreholes in the world currently used for this type of monitoring. Nine of the ground-based climate stations are co-located with deep boreholes, forming 'permafrost observatories.' Deployment of ground-based stations began in 1998 and monitoring of the borehole array has been ongoing since 1979, thus providing more than a decade of surface measurements and three decades of measurements of deep permafrost thermal state. To satisfy some of the needs of the global climate-change community, land managers, and the public, much of the data is now available via real-time telemetry.
Data from the borehole array show that mean surface temperatures have warmed 3-4 K in northern Alaska since 1989. This is a significant fraction of the change projected to occur in this region by 2090 by AOGCMs, suggesting the observations are running well ahead of the models. Data from the ground-based climate stations document a significant warming trend over the last decade, during ALL seasons. Trends of (0.1-0.2 K/yr) are observed in both the ground and in the lower atmosphere. Data from the camera systems are documenting rapid permafrost degradation (~ 20 meters of coastal erosion per year) and providing new insights into erosional processes. We have also begun using the community-based next-generation Weather Research & Forecasting Model (WRF) to better understand the spatial patterns of climate variables (e.g., temperature, rain, snow, wind, cloudiness) and extreme events (e.g., rain-on-snow, high winds) at much higher resolutions (1-km) than can be obtained from the surface-station network.
