Quantifying LongTerm Land Surface and Root Zone Soil Moisture over Tibetan Plateau It is crucial to monitor the dynamics of soil moisture over the Tibetan Plateau, while considering its important role in understanding the landatmosphere interactions and their influences on climate systems (e.g., Eastern Asian Summer Monsoon). However, it is very challenging to have both the surface and root zone soil moisture (SSM and RZSM) over this area, especially the study of feedbacks between soil moisture and climate systems requires longterm (e.g., decadal) datasets. In this study, the SSM data from different sources (satellites, land data assimilation, and insitu measurements) were blended while using triple collocation and least squares method with the constraint of insitu data climatology. A depth scaling was performed based on the blended SSM product, using Cumulative Distribution Function (CDF) matching approach and simulation with Soil Moisture Analytical Relationship (SMAR) model, to estimate the RZSM. The final product is a set of longterm (10 yr) consistent SSM and RZSM product. The intercomparison with other existing SSM and RZSM products demonstrates the credibility of the data blending procedure used in this study and the reliability of the CDF matching method and SMAR model in deriving the RZSM.

How much variation in land surface phenology can climate oscillation modes explain at the scale of mountain pastures in Kyrgyzstan Abstract   Climate oscillation modes can shape weather across the globe due to atmospheric teleconnections. We built on the findings of a recent study to assess whether the impacts of teleconnections are detectable and significant in the early season dynamics of highland pastures across five rayons in Kyrgyzstan. Specifically, since land surface phenology (LSP) has already shown to be influenced by snow cover seasonality and terrain, we investigated here how much more explanatory and predictive power information about climatic oscillation modes might add to explain variation in LSP. We focused on seasonal values of five climate oscillation indices that influence vegetation dynamics in Central Asia. We characterized the phenology in highland pastures with metrics derived from LSP modeling using Landsat NDVI time series together with MODIS land surface temperature (LST) data: Peak Height (PH), the maximum modeled NDVI and Thermal Time to Peak (TTP), the quantity of accumulated growing degreedays based on LST required to reach PH. Next, we calculated two metrics of snow cover seasonality from MODIS snow cover composites: last date of snow (LDoS), and the number of snow covered dates (SCD). For terrain features, we derived elevation, slope, and TRASP index as linearization of aspect. First, we used Spearmanxe2x80x99s rank correlation to assess the geographical differentiation of land surface phenology metrics responses to environmental variables. PH showed weak correlations with TTP (positive in western but negative in eastern rayons), and moderate relationships with LDoS and SCD only in one northeastern rayon. Slope was weakly related to PH, while TRASP showed a consistent moderate negative correlation with PH. A significant but weak negative correlation was found between PH and SCAND JJA, and a significant weak positive correlation with MEI MAM. TTP showed consistently strong negative relationships with LDoS, SCD, and elevation. Very weak positive correlations with TTP were found for EAWR DJF, AMO DJF, and MEI DJF in western rayons only. Second, we used Partial Least Squares regression to investigate the role of oscillation modes altogether. PLS modelling of TTP showed that thermal time accumulation could be explained mostly by elevation and snow cover metrics, leading to reduced models explaining 55 to 70% of observed variation in TTP. Variable selection indicated that NAO JJA, AMO JJA and SCAND MAM had significant relationships with TTP, but their input of predictive power was neglible. PLS models were able to explain up to 29% of variability in PH. SCAND JJA and MEI MAM were shown to be significant predictors, but adding them into models did not influence modeling performance. We concluded the impacts of climate oscillation anomalies were not detectable or significant in mountain pastures using LSP metrics at fine spatial resolution. Rather, at a 30xe2x80xafm resolution, the indirect effects of seasonal climatic oscillations are overridden by terrain influences (mostly elevation) and snow cover timing. Whether climate oscillation mode indices can provide some new and useful information about growing season conditions remains a provocative question, particularly in light of the multiple environmental challenges facing the agropastoralism livelihood in montane Central Asia.