Use of Satellite Soil Moisture to Diagnose Climate Model Representations of European Soil Moisture‐Air Temperature Coupling Strength

Dong, Jianzhi; Crow, Wade T.

doi:10.1029/2018gl080547

Cited by 18 publications

(15 citation statements)

References 37 publications

(60 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They hypothesized that this improved correlation is attributable to the improved temporal accuracy of the SMAP L4 product. Dong and Crow (, ) provided a more rigorous error propagation analysis to test this hypothesis and demonstrated that improved correlation with other independent geophysical observations (e.g., streamflow or air temperature) can be associated with increased soil moisture accuracy. These studies suggest the possibility of using an additional, independent time series—provided it is physically linked to soil moisture—as an instrument for estimating the relative increase in skill associated with the assimilation of satellite‐based observations into a land surface model.…”

Section: Introductionmentioning

confidence: 50%

“…This study extends the correlation analysis by Dong and Crow (, ) and globally evaluates the relative skill of SMAP L4 and OL surface soil moisture estimates. Based on linear, additive error assumptions, the ratio of L4 and OL correlations with independently observed scatterometer‐based surface soil moisture retrievals is shown to be mathematically equivalent to their relative correlations with true soil moisture.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Global Assessment of Added Value in the SMAP Level 4 Soil Moisture Product Relative to Its Baseline Land Surface Model

Dong

Crow

Reichle

et al. 2019

Geophysical Research Letters

Self Cite

View full text Add to dashboard Cite

The Soil Moisture Active Passive (SMAP) level 4 product provides enhanced soil moisture estimates by assimilating SMAP brightness temperature observations into a land surface model. Here, a quantitative estimate of the relative skill of SMAP Level‐4 and model‐only surface soil moisture (vs. true soil moisture) is derived using only one additional noisy (but independent) soil moisture product. The method is applied globally and verified using high‐quality, ground‐based measurements where available. Results demonstrate that assimilating SMAP brightness temperature has relatively little impact in data‐rich areas like the United States and Europe. In contrast, much larger improvement is observed in data‐sparse regions, including much of Africa and central Australia, where model‐only simulations are disproportionately impacted by low‐quality model forcing. Therefore, ground validation conducted in data‐rich areas does not adequately sample the added value of SMAP data assimilation for data‐sparse regions and substantially underestimates the added skill provided by the SMAP level 4 system.

show abstract

Section: Introductionmentioning

confidence: 50%

Section: Introductionmentioning

confidence: 99%

A Global Assessment of Added Value in the SMAP Level 4 Soil Moisture Product Relative to Its Baseline Land Surface Model

Dong

Crow

Reichle

et al. 2019

Geophysical Research Letters

Self Cite

View full text Add to dashboard Cite

show abstract

“…To date, evaluating model ρ estimates has proven difficult. Precise, large‐scale estimates of SM and ET data are generally unavailable (Mueller & Seneviratne, 2012), and ρ metrics based on error‐prone SM and ET remote sensing (RS) retrievals can be strongly biased (Dong & Crow, 2018a, 2018b, 2019). However, Crow et al (2015) introduced a statistical approach to debias RS‐based ρ estimates.…”

Section: Introductionmentioning

confidence: 99%

“…Atmospheric modeling error can potentially lead to a biased ρ representation in ESMs (Dong & Crow, 2018b;Vautard et al, 2013). For instance, a low bias in precipitation will tend to increase ET water stress-leading to a positive bias in ρ. Additionally, a primary error source for modeled ρ is our imperfect knowledge of land surface parameters.…”

Section: Introductionmentioning

confidence: 99%

Soil Evaporation Stress Determines Soil Moisture‐Evapotranspiration Coupling Strength in Land Surface Modeling

Dong

Dirmeyer

Lei

et al. 2020

Geophysical Research Letters

Self Cite

View full text Add to dashboard Cite

Model-based estimates of soil moisture (SM)-evapotranspiration (ET) coupling strength (ρ) vary widely and are prone to bias. Here we apply numerical modeling and remote sensing to identify the process-level source of modeled ρ bias with the goal of improving the fidelity of current Earth system models. Results illustrate that modeled ρ is most strongly determined by soil evaporation (E) stress, and (generally positive) ρ modeling bias is attributable to the oversimplification of soil texture impacts on E stress. Based on new remotely sensed estimates of ρ, we demonstrate that removing ρ bias via a single optimized E stress parameter leads to improved ET accuracy and resolves a well-known modeling bias in the partitioning of ET into E and T. As such, we highlight the importance of the stress function relating E and SM and its central role in regulating land-atmosphere coupling processes impacting local climate. Plain Language Summary The interaction of soil moisture (SM) and evapotranspiration (ET) has been identified as a key process for accurately capturing land-atmosphere coupling. However, recent work suggests that the representation of SM-ET coupling in existing Earth system models (ESMs) is biased and can reduce the reliability of ESM-projected climate change scenarios. Based on remote sensing and land surface modeling, we identify soil evaporation (E) stress as the key contributor to SM-ET coupling uncertainty, even in areas where transpiration dominates total ET, and note that the representation of E stress is typically oversimplified in ESMs. Therefore, our analysis suggests that a more realistic representation of land E stress should be a priority for future ESM development.

show abstract

“…There are major differences between models in how forcings are translated into energy flux responses, and thus differences in modeled land surface responsiveness to forcing (Dong & Crow, 2018; Lei et al., 2018; Short Gianotti, Rigden, et al., 2019). This ultimately results in divergence of future projections of the land and atmosphere states (Berg et al., 2015; Berg & Sheffield, 2018; Guo et al., 2006).…”

Section: Introductionmentioning

confidence: 99%

Observed Landscape Responsiveness to Climate Forcing

Feldman

Gianotti

Trigo

et al. 2022

Water Resources Research

View full text Add to dashboard Cite

Climate variability and change shift environmental conditions on global land surfaces, creating uncertainties in predicting hydrologic flows, crop yields, and land carbon uptake. Land surfaces can present varying degrees of inertia to atmospheric forcing variability (e.g., precipitation). This study asks: are regions with the most variable environmental forcing necessarily the regions with the largest land surface variability? Specifically, it seeks to determine why land surfaces show varying responsiveness to environmental forcing. The degree to which and the mechanisms for how landscapes modulate the forcing are evaluated using a decade‐long satellite observation record of Africa's diverse climates. Surface responsiveness is quantified using intra‐seasonal energy flux variability, based on the observed diurnal temperature amplitude. We map the responsiveness and analyze the underlying mechanisms over intra‐seasonal timescales (especially interstorms). We show that, at a location, land surface responsiveness is dependent on the soil moisture distribution and the nonlinear relationship between energy fluxes and soil moisture. Land surfaces with greater responsiveness to climate are those with soil moisture distributions that span the threshold between evaporation regimes and spend most of their time in the water‐limited regime. Consequently, surface responsiveness mechanisms drive land surface variability beyond high climatic variability. Since we find these results to hold from intra‐seasonal to interannual timescales, we expect that these responsive regions will be most vulnerable to long‐term shifts in climate forcing. The quantification of these phenomena and determination of their geographic distributions based on observations can help assess land surface models used to evaluate hydrologic consequences of climate change.

show abstract

Use of Satellite Soil Moisture to Diagnose Climate Model Representations of European Soil Moisture‐Air Temperature Coupling Strength

Cited by 18 publications

References 37 publications

A Global Assessment of Added Value in the SMAP Level 4 Soil Moisture Product Relative to Its Baseline Land Surface Model

A Global Assessment of Added Value in the SMAP Level 4 Soil Moisture Product Relative to Its Baseline Land Surface Model

Soil Evaporation Stress Determines Soil Moisture‐Evapotranspiration Coupling Strength in Land Surface Modeling

Observed Landscape Responsiveness to Climate Forcing

Contact Info

Product

Resources

About