This work investigates how the integrated land use of northern South America has affected the present day regional patterns of hydrology. A model of the terrestrial ecosystems (ecosystem demography model 2: ED2) is combined with an atmospheric model (Brazilian Regional Atmospheric Modeling System: BRAMS). Two realizations of the structure and composition of terrestrial vegetation are used as the sole differences in boundary conditions that drive two simulations. One realization captures the present day vegetation condition that includes deforestation and land conversion, the other is an estimate of the potential structure and composition of the region's vegetation without human influence. Model output is assessed for differences in resulting hydrometeorology.

The simulations suggest that the history of land conversion in northern South America is not associated with a significant precipitation bias in the northern part of the continent, but has shown evidence of a negative bias in mean regional evapotranspiration and a positive bias in mean regional runoff. Also, negative anomalies in evaporation rates showed pattern similarity with areas where deforestation has occurred. In the central eastern Amazon there was an area where deforestation and abandonment had lead to an overall reduction of above-ground biomass, but this was accompanied by a shift in forest composition towards early successional functional types and grid-average-patterned increases in annual transpiration.

Anomalies in annual precipitation showed mixed evidence of consistent patterning. Two focus areas were identified where more consistent precipitation anomalies formed, one in the Brazilian state of Pará where a dipole pattern formed, and one in the Bolivian Gran Chaco, where a negative anomaly was identified. These locations were scrutinized to understand the basis of their anomalous hydrometeorologic response. In both cases, deforestation led to increased total surface albedo, driving decreases in net radiation, boundary layer moist static energy and ultimately decreased convective precipitation. In the case of the Gran Chaco, decreased precipitation was also a result of decreased advective moisture transport, indicating that differences in local hydrometeorology may manifest via teleconnections with the greater region.

Recent progress in satellite observations has provided unprecedented opportunities to monitor vegetation activity at global scale. However, a major challenge in fully utilizing remotely sensed data to constrain land surface models (LSMs) lies in inconsistencies between simulated and observed quantities. For example, gross primary productivity (GPP) and transpiration (T) that traditional LSMs simulate are not directly measurable from space, although they can be inferred from spaceborne observations using assumptions that are inconsistent with those LSMs. In comparison, canopy reflectance and fluorescence spectra that satellites can detect are not modeled by traditional LSMs. To bridge these quantities, we presented an overview of the next generation land model developed within the Climate Modeling Alliance (CliMA), and simulated global GPP, T, and hyperspectral canopy radiative transfer (RT; 400–2,500 nm for reflectance, 640–850 nm for fluorescence) at hourly time step and 1° spatial resolution using CliMA Land. CliMA Land predicts vegetation indices and outgoing radiances, including solar-induced chlorophyll fluorescence (SIF), normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and near infrared reflectance of vegetation (NIRv) for any given sun-sensor geometry. The spatial patterns of modeled GPP, T, SIF, NDVI, EVI, and NIRv correlate significantly with existing data-driven products (mean R2 = 0.777 for 9 products). CliMA Land would be also useful in high temporal resolution simulations, for example, providing insights into when GPP, SIF, and NIRv diverge.

We analyze the North American budget for carbon monoxide using data for CO and formaldehyde concentrations from tall towers and aircraft in a model-data assimilation framework. The Stochastic Time-Inverted Lagrangian Transport model for CO (STILT-CO) determines local to regional-scale CO contributions associated with production from fossil fuel combustion, biomass burning, and oxidation of volatile organic compounds (VOCs) using an ensemble of Lagrangian particles driven by high resolution assimilated meteorology. In many cases, the model demonstrates high fidelity simulations of hourly surface data from tall towers and point measurements from aircraft, with somewhat less satisfactory performance in coastal regions and when CO from large biomass fires in Alaska and the Yukon Territory influence the continental US. Inversions of STILT-CO simulations for CO and formaldehyde show that current inventories of CO emissions from fossil fuel combustion are significantly too high, by almost a factor of three in summer and a factor two in early spring, consistent with recent analyses of data from the INTEX-A aircraft program. Formaldehyde data help to show that sources of CO from oxidation of CH4 and other VOCs represent the dominant sources of CO over North America in summer. © Author(s) 2008.

Abstract. We analyze the North American budget for carbon monoxide using data for CO and formaldehyde concentrations from tall towers and aircraft in a model-data assimilation framework. The Stochastic Time-Inverted, Lagrangian Transport model for CO (STILT-CO) determines local to regional-scale CO contributions associated with production from fossil fuel combustion, biomass burning, and oxidation of volatile organic compounds (VOCs) using an ensemble of Lagrangian particles driven by high resolution assimilated meteorology. In most cases, the model demonstrates high fidelity simulations of hourly surface data from tall towers and point measurements from aircraft, with somewhat less satisfactory performance in coastal regions and when CO from large biomass fires in Alaska and the Yukon Territory influence the continental US. Inversions of STILT-CO simulations for CO and formaldehyde show that current inventories of CO emissions from fossil fuel combustion are significantly too high, by almost a factor of three in summer and a factor two in early spring, consistent with recent analyses of data from the INTEX-A aircraft program. Formaldehyde data help to show that sources of CO from oxidation of CH4 and other VOCs represent the dominant sources of CO over North America in summer.

Earth system models (ESMs) typically simplify the representation of land surface spectral albedo to two values, which correspond to the photosynthetically active radiation (PAR, 400–700 nm) and the near infrared (NIR, 700–2,500 nm) spectral bands. However, the availability of hyperspectral observations now allows for a more direct retrieval of ecological parameters and reduction of uncertainty in surface reflectance. To investigate sensitivity and quantify biases of incorporating hyperspectral albedo information into ESMs, we examine how shortwave soil albedo affects surface radiative forcing and simulations of the carbon and water cycles. Results reveal that the use of two broadband values to represent soil albedo can introduce systematic radiative-forcing differences compared to a hyperspectral representation. Specifically, we estimate soil albedo biases of ±0.2 over desert areas, which can result in spectrally integrated radiative forcing divergences of up to 30 W m−2, primarily due to discrepancies in the blue (404–504 nm) and far-red (702–747 nm) regions. Furthermore, coupled land-atmosphere simulations indicate a significant difference in net solar flux at the top of the atmosphere (>3.3 W m−2), which can impact global energy fluxes, rainfall, temperature, and photosynthesis. Finally, simulations show that considering the hyperspectrally resolved soil reflectance leads to increased maximum daily temperatures under current and future CO2 concentrations.

A case study of convective development in the Southwest Amazon region during the Wet Season Atmospheric Mesoscale Campaign (WETAMC) and Tropical Rainfall Measuring Mission (TRMM)/Large-Scale Biosphere-Atmosphere (LBA) Experiment in Amazonia is presented. The convective development during 7 February 1999 is shown to occur during a period of very weak large-scale forcing in the presence of topography and deforestation. The available data include dual Doppler radar analysis, radiosonde launches, and surface and boundary layer observations. The observational analysis is complemented with a series of model simulations using the RAMS with 2-km resolution over a 300 km × 300 km area forced by a morning radiosonde profile. A comparison of the observed and simulated thermodynamic transformation of the boundary layer and of the formation of convective lines, and of their kinematic and microphysical properties is presented. It is shown that only a few very deep and intense convective cells are necessary to explain the overall precipitating line formation and that discrete propagation and coupling with upper atmosphere circulations may explain the appearance of several lines. The numerical simulation indicates that topography may be the cause of initial convective development, although later on the convective line is parallel to the midlevel shear. There are indications that small-scale deforestation may have an effect on increasing rainfall in the wet season when the large-scale forcing is very weak. Copyright 2002 by the American Geophysical Union.

The High-Altitude Water Cherenkov Gamma Ray Observatory (HAWC) is under construction 4100 meters above sea level at Sierra Negra, Mexico. We describe the design and cabling of the detector, the characterization of the photomultipliers, and the timing calibration system. We also outline a next-generation detector based on the water Cherenkov technique.