Influence of Land Cover and Soil Moisture based Brown Ocean Effect on an Extreme Rainfall Event from a Louisiana Gulf Coast Tropical System.

Extreme flooding over southern Louisiana in mid-August of 2016 resulted from an unusual tropical low that formed and intensified over land. We used numerical experiments to highlight the role of the 'Brown Ocean' effect (where saturated soils function similar to a warm ocean surface) on intensification and it's modulation by land cover change. A numerical modeling experiment that successfully captured the flood event (control) was modified to alter moisture availability by converting wetlands to open water, wet croplands, and dry croplands.

Estimating Evapotranspiration From Satellite Using Easily Obtainable Variables: A case study over the Southern Great Plains, U.S.A.

Evapotranspiration (ET) is a critical component of the Earth's water budget, a critical modulator of land-atmosphere (L-A) interactions, and also plays a crucial role in managing the Earth's energy balance. In this study, the feasibility of generating spatially-continuous daily evaporative fraction (EF) and ET from minimal remotely-sensed and meteorological inputs in a trapezoidal framework is demonstrated. A total of four variables, Normalized Difference Vegetation Index (NDVI), Land surface temperature ( ), gridded daily average temperature ( ) and elevation (z) are required to estimate EF.

Modeling regional pollution transport events during KORUS-AQ: Progress and challenges in improving representation of land-atmosphere feedbacks.

This study evaluates the impact of assimilating soil moisture data from NASA's Soil Moisture Active Passive (SMAP) on short-term regional weather and air quality modeling in East Asia during the Korea-US Air Quality Study (KORUS-AQ) airborne campaign. SMAP data are assimilated into the Noah land surface model using an ensemble Kalman filter approach in the Land Information System framework, which is semi-coupled with the NASA-Unified Weather Research and Forecasting model with online chemistry (NUWRF-Chem). With SMAP assimilation included, water vapor and carbon monoxide (CO) transport from northern-central China transitional climate zones to South Korea is better represented in NUWRF-Chem during two studied pollution events.

Heterogeneity in warm-season land-atmosphere coupling over the U.S. Southern Great Plains.

Heterogeneity in warm-season (May-August) land-atmosphere (LA) coupling is quantified with the long-time, multiple-station measurements from the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) program and the moderate-resolution imaging spectroradiometer (MODIS) satellite remote sensing at the Southern Great Plains (SGP).

Verification of land-atmosphere coupling in forecast models, reanalyses and land surface models using flux site observations.

We confront four model systems in three configurations (LSM, LSM+GCM, and reanalysis) with global flux tower observations to validate states, surface fluxes, and coupling indices between land and atmosphere. Models clearly under-represent the feedback of surface fluxes on boundary layer properties (the atmospheric leg of land-atmosphere coupling), and may over-represent the connection between soil moisture and surface fluxes (the terrestrial leg). Models generally under-represent spatial and temporal variability relative to observations, which is at least partially an artifact of the differences in spatial scale between model grid boxes and flux tower footprints.

Utility of Satellite Remote Sensing for Land-Atmosphere Coupling and Drought Metrics.

Feedbacks between the land and the atmosphere can play an important role in the water cycle and a number of studies have quantified Land-Atmosphere (L-A) interactions and feedbacks through observations and prediction models. Due to the complex nature of L-A interactions, the observed variables are not always available at the needed temporal and spatial scales. This work derives the Coupling Drought Index (CDI) solely from satellite data and evaluates the input variables and the resultant CDI against in-situ data and reanalysis products.

The impact of anthropogenic land use and land cover change on regional climate extremes.

Land surface processes modulate the severity of heat waves, droughts, and other extreme events. However, models show contrasting effects of land surface changes on extreme temperatures. Here, we use an earth system model from the Geophysical Fluid Dynamics Laboratory to investigate regional impacts of land use and land cover change on combined extremes of temperature and humidity, namely aridity and moist enthalpy, quantities central to human physiological experience of near-surface climate.

Assessment of Irrigation Physics in a Land Surface Modeling Framework using Non-Traditional and Human-Practice Datasets.

Irrigation increases soil moisture, which in turn controls water and energy fluxes from the land surface to the planetary boundary layer and determines plant stress and productivity. Therefore, developing a realistic representation of irrigation is critical to understanding land-atmosphere interactions in agricultural areas. Irrigation parameterizations are becoming more common in land surface models and are growing in sophistication, but there is difficulty in assessing the realism of these schemes, due to limited observations (e.

Confronting weather and climate models with observational data from soil moisture networks over the United States.

Four land surface models in uncoupled and coupled configurations are compared to observations of daily soil moisture from 19 networks in the conterminous United States to determine the viability of such comparisons and explore the characteristics of model and observational data. First, observations are analyzed for error characteristics and representation of spatial and temporal variability. Some networks have multiple stations within an area comparable to model grid boxes; for those we find that aggregation of stations before calculation of statistics has little effect on estimates of variance, but soil moisture memory is sensitive to aggregation.