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MODIS-Derived Snowmelt Timing Maps, Version 2

Submitted by ORNL DAAC Staff on 2020-01-30
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Mean snowmelt Day of Year (DOY) for the years 2001-2018 for North America. The DOY values for snowmelt timing were identified by observing the transition from snow-cover to no-snow for individual MODIS pixels throughout the annual melt period.

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The second version of snowmelt timing maps for North America is available for the period 2001 through 2018.

Arctic Whole Air Samples

Submitted by ORNL DAAC Staff on 2020-01-30
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Arctic-CAP flights consisted of vertical profile maneuvers from near the surface to 6 km altitude around the ABoVE domain each month. Profiles were flown at each of the 25 locations listed across the top of this figure.

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Atmospheric gas concentrations collected during the Arctic Carbon Aircraft Profile (Arctic-CAP) campaigns are now available.

High Latitude Blue Sky Albedo

Submitted by ORNL DAAC Staff on 2020-01-27
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Daily mean shortwave blue sky albedo for February 28, 2000. Blue sky refers to albedo calculated under real-world conditions with a combination of both diffuse and direct lighting based on atmospheric and view-geometry conditions.

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Daily “blue sky” shortwave albedo data were derived from the MODIS bi-directional reflectance distribution function.

Historical and Future Leaching from the Mississippi River Basin

Submitted by ORNL DAAC Staff on 2020-01-20
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Spatial distribution of mean dissolved inorganic carbon (DIC) leaching (g c/m2/yr) in the 2090s estimated by S(ALL) simulation experiment (including future climate, elevated CO2, and land use changes). Climate change scenarios were derived from three climate models named CCSM3 (panels a and b), ECHAM (c and d), and CCCMA (e and f) under high (A2) and low (B1) emission scenarios, respectively.

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New data from the Carbon Monitoring System (CMS) offers insights into how environmental factors affect the dynamics of leaching.

Predicted Annual Soil Respiration from the Carbon Monitoring System

Submitted by ORNL DAAC Staff on 2020-01-13
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A global map of predicted annual soil respiration (Rs) at 1 km spatial resolution created by applying the QRF model to gridded covariates. Right is a plot of the latitudinal mean predicted annual Rs.

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A machine learning approach was used to derive the predicted annual soil respiration and uncertainty at a 1 km spatial resolution.