This proposal addresses the tier 2 science question (3.6) of the terrestrial ecology solicitation (a.4): "how are the magnitudes, fates, and land-atmosphere exchanges of carbon pools responding to…

This federal contract award, valued at $763,652, was granted by the National Aeronautics and Space Administration (NASA) Shared Services Center to the Woodwell Climate Research Center, a non-profit research organization. The contract aims to measure and understand winter respiration, a critical component of the Arctic carbon cycle that is not well quantified. The objectives are to 1) continuously measure winter respiration at sites across the permafrost region, 2) examine the drivers of winter respiration, and 3) combine field data with remote sensing to estimate regional carbon dioxide emissions during the non-growing season. This work supports NASA's Terrestrial Ecology program and seeks to address key uncertainties around how Arctic carbon pools are responding to environmental change. The contract does not have a set-aside designation, and Woodwell Climate Research Center has received previous federal grants and contracts from agencies like the National Science Foundation to study climate change impacts in northern ecosystems.

This proposal addresses the tier 2 science question (3.6) of the terrestrial ecology solicitation (a.4): "how are the magnitudes, fates, and land-atmosphere exchanges of carbon pools responding to environmental change, and what are the biogeochemical mechanisms driving these changes?" over the past decades, surface air temperatures in the arctic have increased at approximately twice the global rate, and climate models project that this rate of warming will continue through the century, with the greatest warming occurring during the winter months. An increase in wintertime temperature may reduce belowground carbon storage due to enhanced microbial respiration during the snow-covered period. Carbon dioxide (CO2) emissions during the winter and shoulder seasons (fall and spring) are an important component of annual respiratory loss, yet there is large uncertainty in local and regional estimates of CO2 emissions during the non-growing season. Carbon losses during the snow covered period can be as great as or equal to net growing season carbon uptake, shifting tundra ecosystems from carbon sinks during the growing season to sources on an annual basis. Therefore, refining winter respiration estimates and understanding the drivers of winter respiration are critical for understanding current and future carbon emissions from the arctic. The objectives of this proposal are to 1.) measure winter respiration continuously at sites across the permafrost region, 2) examine the drivers of winter respiration, and 3) combine field measurements with remotely sensed data sets and products to estimate regional CO2 emissions during the non-growing season. We will instrument sites across the above study domain with newly-developed forced diffusion soil efflux sensors (FDS), a low-power technique with high temporal resolution and high reliability for harsh deployment environments. We will also collect continuous automated measurements of key environmental drivers (e.g., air temperature, snow cover, soil moisture, soil temperature) and measure vegetation and soil characteristics at the plot level, and we will extend plot-level measurements of respiration drivers to larger spatial scales consistent with satellite remote sensing data retrievals. Sites will be selected to span the range of permafrost regions from continuous to sporadic to allow US to address questions about winter respiration under different thaw regimes, including those influenced by fire disturbance and related thermal erosion, changes in active layer depth, and thermokarst. The site-level flux measurements will be used with a range of existing multi-scale satellite data sets and products, as well as newly generated products where needed (particularly outside the growing season), to scale the field measurements spatially and temporally, thereby estimating CO2 emissions from winter respiration across the landscape. Linking to satellite data sets will be done primarily via indirect proxies such as vegetation properties (cover, density, composition) and land surface temperature. Additional landscape variables such as topography, soil properties and surficial geology will also be used as predictor variables in spatially explicit statistical ensemble models (i.e. random forest). Image segmentation approaches will also be used as a means to associate field measurements with spatial data and scale to the landscape.