Climate change is leading to rapid shifts in seasonality in the himalaya.

Climate change has significantly impacted vegetation phenology across the globe with vegetation experiencing an advance in the spring green-up phases and a delay in fall senescence. However, some studies from high latitudes and high elevations have instead shown delayed spring phenology, owing to a lack of chilling fulfillment and altered snow cover and photoperiods. Here we use the MODIS satellite-derived view-angle corrected surface reflectance data (MCD43A4) to document the four phenological phases in the high elevations of the Sikkim Himalaya and compared the phenological trends between below-treeline zones and above-treeline zones. This analysis of remotely sensed data for the study period (2001-2017) reveals considerable shifts in the phenology of the Sikkim Himalaya. Advances in the spring start of the season phase (SOS) were more pronounced than delays in the dates for maturity (MAT), senescence (EOS), and advanced dormancy (DOR). The SOS significantly advanced by 21.3 days while the MAT and EOS were delayed by 15.7 days and 6.5 days respectively over the 17-year study period. The DOR showed an advance of 8.2 days over the study period. The region below the treeline showed more pronounced shifts in phenology with respect to an advanced SOS and a delayed EOS and DOR that above treeline. The MAT, however, showed a greater delay in the zone above the treeline than below. Lastly, unlike other studies from high elevations, there is no indication that winter chilling requirements are driving the spring phenology in this region. We discuss four possible explanations for why vegetation phenology in the high elevations of the Eastern Himalaya may exhibit trends independent of chilling requirements and soil moisture due to mediation by snow cover.

Carbon response of tundra ecosystems to advancing greenup and snowmelt in Alaska.

The ongoing disproportionate increases in temperature and precipitation over the Arctic region may greatly alter the latitudinal gradients in greenup and snowmelt timings as well as associated carbon dynamics of tundra ecosystems. Here we use remotely-sensed and ground-based datasets and model results embedding snowmelt timing in phenology at seven tundra flux tower sites in Alaska during 2001-2018, showing that the carbon response to early greenup or delayed snowmelt varies greatly depending upon local climatic limits. Increases in net ecosystem productivity (NEP) due to early greenup were amplified at the higher latitudes where temperature and water strongly colimit vegetation growth, while NEP decreases due to delayed snowmelt were alleviated by a relief of water stress. Given the high likelihood of more frequent delayed snowmelt at higher latitudes, this study highlights the importance of understanding the role of snowmelt timing in vegetation growth and terrestrial carbon cycles across warming Arctic ecosystems.

Climate change decreases the cooling effect from postfire albedo in boreal North America.

Fire is a primary disturbance in boreal forests and generates both positive and negative climate forcings. The influence of fire on surface albedo is a predominantly negative forcing in boreal forests, and one of the strongest overall, due to increased snow exposure in the winter and spring months. Albedo forcings are spatially and temporally heterogeneous and depend on a variety of factors related to soils, topography, climate, land cover/vegetation type, successional dynamics, time since fire, season, and fire severity. However, how these variables interact to influence albedo is not well understood, and quantifying these relationships and predicting postfire albedo becomes increasingly important as the climate changes and management frameworks evolve to consider climate impacts. Here we developed a MODIS-derived 'blue sky' albedo product and a novel machine learning modeling framework to predict fire-driven changes in albedo under historical and future climate scenarios across boreal North America. Converted to radiative forcing (RF), we estimated that fires generate an annual mean cooling of -1.77 ± 1.35 W/m2 from albedo under historical climate conditions (1971-2000) integrated over 70 years postfire. Increasing postfire albedo along a south-north climatic gradient was offset by a nearly opposite gradient in solar insolation, such that large-scale spatial patterns in RF were minimal. Our models suggest that climate change will lead to decreases in mean annual postfire albedo, and hence a decreasing strength of the negative RF, a trend dominated by decreased snow cover in spring months. Considering the range of future climate scenarios and model uncertainties, we estimate that for fires burning in the current era (2016) the cooling effect from long-term postfire albedo will be reduced by 15%-28% due to climate change.

Monitoring land surface albedo and vegetation dynamics using high spatial and temporal resolution synthetic time series from Landsat and the MODIS BRDF/NBAR/albedo product.

Seasonal vegetation phenology can significantly alter surface albedo which in turn affects the global energy balance and the albedo warming/cooling feedbacks that impact climate change. To monitor and quantify the surface dynamics of heterogeneous landscapes, high temporal and spatial resolution synthetic time series of albedo and the enhanced vegetation index (EVI) were generated from the 500 m Moderate Resolution Imaging Spectroradiometer (MODIS) operational Collection V006 daily BRDF/NBAR/albedo products and 30 m Landsat 5 albedo and near-nadir reflectance data through the use of the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM). The traditional Landsat Albedo (Shuai et al., 2011) makes use of the MODIS BRDF/Albedo products (MCD43) by assigning appropriate BRDFs from coincident MODIS products to each Landsat image to generate a 30 m Landsat albedo product for that acquisition date. The available cloud free Landsat 5 albedos (due to clouds, generated every 16 days at best) were used in conjunction with the daily MODIS albedos to determine the appropriate 30 m albedos for the intervening daily time steps in this study. These enhanced daily 30 m spatial resolution synthetic time series were then used to track albedo and vegetation phenology dynamics over three Ameriflux tower sites (Harvard Forest in 2007, Santa Rita in 2011 and Walker Branch in 2005). These Ameriflux sites were chosen as they are all quite nearby new towers coming on line for the National Ecological Observatory Network (NEON), and thus represent locations which will be served by spatially paired albedo measures in the near future. The availability of data from the NEON towers will greatly expand the sources of tower albedometer data available for evaluation of satellite products. At these three Ameriflux tower sites the synthetic time series of broadband shortwave albedos were evaluated using the tower albedo measurements with a Root Mean Square Error (RMSE) less than 0.013 and a bias within the range of ±0.006. These synthetic time series provide much greater spatial detail than the 500 m gridded MODIS data, especially over more heterogeneous surfaces, which improves the efforts to characterize and monitor the spatial variation across species and communities. The mean of the difference between maximum and minimum synthetic time series of albedo within the MODIS pixels over a subset of satellite data of Harvard Forest (16 km by 14 km) was as high as 0.2 during the snow-covered period and reduced to around 0.1 during the snow-free period. Similarly, we have used STARFM to also couple MODIS Nadir BRDF Adjusted Reflectances (NBAR) values with Landsat 5 reflectances to generate daily synthetic times series of NBAR and thus Enhanced Vegetation Index (NBAR-EVI) at a 30 m resolution. While normally STARFM is used with directional reflectances, the use of the view angle corrected daily MODIS NBAR values will provide more consistent time series. These synthetic times series of EVI are shown to capture seasonal vegetation dynamics with finer spatial and temporal details, especially over heterogeneous land surfaces.

Early spring post-fire snow albedo dynamics in high latitude boreal forests using Landsat-8 OLI data.

Taking advantage of the improved radiometric resolution of Landsat-8 OLI which, unlike previous Landsat sensors, does not saturate over snow, the progress of fire recovery progress at the landscape scale (< 100m) is examined. High quality Landsat-8 albedo retrievals can now capture the true reflective and layered character of snow cover over a full range of land surface conditions and vegetation densities. This new capability particularly improves the assessment of post-fire vegetation dynamics across low- to high- burn severity gradients in Arctic and boreal regions in the early spring, when the albedos during recovery show the greatest variation. We use 30 m resolution Landsat-8 surface reflectances with concurrent coarser resolution (500m) MODIS high quality full inversion surface Bidirectional Reflectance Distribution Functions (BRDF) products to produce higher resolution values of surface albedo. The high resolution full expression shortwave blue sky albedo product performs well with an overall RMSE of 0.0267 between tower and satellite measures under both snow-free and snow-covered conditions. While the importance of post-fire albedo recovery can be discerned from the MODIS albedo product at regional and global scales, our study addresses the particular importance of early spring post-fire albedo recovery at the landscape scale by considering the significant spatial heterogeneity of burn severity, and the impact of snow on the early spring albedo of various vegetation recovery types. We found that variations in early spring albedo within a single MODIS gridded pixel can be larger than 0.6. Since the frequency and severity of wildfires in Arctic and boreal systems is expected to increase in the coming decades, the dynamics of albedo in response to these rapid surface changes will increasingly impact the energy balance and contribute to other climate processes and physical feedback mechanisms. Surface radiation products derived from Landsat-8 data will thus play an important role in characterizing the carbon cycle and ecosystem processes of high latitude systems.

Soil and geography are more important determinants of indigenous arbuscular mycorrhizal communities than management practices in Swiss agricultural soils.

Arbuscular mycorrhizal fungi (AMF) are ubiquitous soil fungi, forming mutualistic symbiosis with a majority of terrestrial plant species. They are abundant in nearly all soils, less diverse than soil prokaryotes and other intensively studied soil organisms and thus are promising candidates for universal indicators of land management legacies and soil quality degradation. However, insufficient data on how the composition of indigenous AMF varies along soil and landscape gradients have hampered the definition of baselines and effect thresholds to date. Here, indigenous AMF communities in 154 agricultural soils collected across Switzerland were profiled by quantitative real-time PCR with taxon-specific markers for six widespread AMF species. To identify the key determinants of AMF community composition, the profiles were related to soil properties, land management and site geography. Our results indicate a number of well-supported dependencies between abundances of certain AMF taxa and soil properties such as pH, soil fertility and texture, and a surprising lack of effect of available soil phosphorus on the AMF community profiles. Site geography, especially the altitude and large geographical distance, strongly affected AMF communities. Unexpected was the apparent lack of a strong land management effect on the AMF communities as compared to the other predictors, which could be due to the rarity of highly intensive and unsustainable land management in Swiss agriculture. In spite of the extensive coverage of large geographical and soil gradients, we did not identify any taxon suitable as an indicator of land use among the six taxa we studied.