Publications

The sensitivity of global and regional climate to changes in vegetation density is investigated using a coupled biosphere–atmosphere model. The magnitude of the vegetation changes and their spatial distribution are based on natural decadal variability of the normalized difference vegetation index (NDVI). Different scenarios using maximum and minimum vegetation cover were derived from satellite records spanning the period 1982–90. Albedo decreased in the northern latitudes and increased in the Tropics with increased NDVI. The increase in vegetation density revealed that the vegetation's physiological response was constrained by the limits of the available water resources. The difference between the maximum and minimum vegetation scenarios resulted in a 46% increase in absorbed visible solar radiation and a similar increase in gross photosynthetic CO2 uptake on a global annual basis. This increase caused the canopy transpiration and interception fluxes to increase and reduced those from the soil. The redistribution of the surface energy fluxes substantially reduced the Bowen ratio during the growing season, resulting in cooler and moister near-surface climate, except when soil moisture was limiting. Important effects of increased vegetation on climate are a cooling of about 1.8 K in the northern latitudes during the growing season and a slight warming during the winter, which is primarily due to the masking of high albedo of snow by a denser canopy; and a year-round cooling of 0.8 K in the Tropics. These results suggest that increases in vegetation density could partially compensate for parallel increases in greenhouse warming. Increasing vegetation density globally caused both evapotranspiration and precipitation to increase. Evapotranspiration, however, increased more than precipitation, resulting in a global soil-water deficit of about 15%. A spectral analysis on the simulated results showed that changes in the state of vegetation could affect the low-frequency modes of the precipitation distribution and might reduce its low-frequency variability in the Tropics while increasing it in northern latitudes.

The very-high-resolution commercial satellite constellation of Maxar offers unique opportunities for a wide range of Earth science research and applications. The key to their widespread and effective use is stable and consistent calibration. In this work, we characterized the long-term calibration trends and cross-calibration coefficients for the four Maxar satellites (GeoEye-1, QuickBird-2, WorldView-2, and WorldView-3) using the Multi-Angle Implementation of Atmospheric Correction (MAIAC) processing technique. Utilizing MAIAC MODIS atmosphere and surface products, we calculated top-of-atmosphere (TOA) reflectance for the Blue, Green, Red, and NIR bands over Libya-4 desert site. To ensure data consistency, we applied geometric normalization to account for variations in TOA reflectance arising from different view geometries. Additionally, a spatial transfer technique was employed to increase the number of samples and yield more robust statistical trend analysis. Our analysis revealed that half of the bands exhibited statistically significant calibration trends. These trends were found to be 2-3 times higher in magnitude compared to those observed in the early Collection 6 MODIS. After detrending, Maxar sensors were cross-calibrated to MODIS Aqua, considered as a calibration standard. In this process, DESIS hyperspectral measurements were used for spectral conversion required to align Maxar with MODIS bands. The cross-calibration analysis shows that GeoEye-1, WorldView-2, and WorldView-3 were systematically higher than MODIS Aqua by 2-4% in the Blue, Green, and NIR, and by 7-8% in the Red. De-trending and cross-calibration to MODIS Aqua effectively transforms the Maxar constellation into a common sensor system enhancing spatiotemporal coverage and broadening the potential range of applications.

Dryland ecosystems cover 40% of our planet’s land surface, support the lives of billions of people, and are responding dramatically to the combined effects of climate and land use change. These expansive and diverse systems also dominate core aspects of Earth’s climate, storing and exchanging vast amounts of water, carbon, and energy with the atmosphere. Despite the indispensable natural resources and ecosystem services provided by drylands and their high vulnerability to change, drylands are one of the most, if not the most, poorly understood ecosystem types. Such lack of study has been in part due to incorrect historical assumptions that drylands are unproductive “wastelands”. This lack of understanding results in notably poor model representation and forecasting capacity, hindering our representation and decision making for these vulnerable ecosystems. The NASA Terrestrial Ecology Program solicited proposals for a multi-year field campaign, of which Adaptation and Response in Drylands (ARID) was one of two scoping studies selected. With the goal of gathering input from the scientific and data end-user communities, we provide an overview of our ARID kick-off meeting with over 300 in-person and virtual participants held in October 2023 at the University of Arizona. This meeting gathered insights from public and private data end-users and scientists. We also report on follow-up activities that have taken place since then, including town halls, community surveys, and international engagements.

El Nino/Southern Oscillation (ENSO) related anomalies were analyzed using a combination of satellite measurements of elevated sea surface temperatures, and subsequent elevated rainfall and satellite derived normalized difference vegetation index data. A Rift Valley fever risk mapping model using these climate data predicted areas where outbreaks of Rift Valley fever in humans and animals were expected and occurred in the Horn of Africa from December 2006 to May 2007. The predictions were subsequently confirmed by entomological and epidemiological field investigations of virus activity in the areas identified as at risk. Accurate spatial and temporal predictions of disease activity, as it occurred first in southern Somalia and then through much of Kenya before affecting northern Tanzania, provided a 2 to 6 week period of warning for the Horn of Africa that facilitated disease outbreak response and mitigation activities. This is the first prospective prediction of a Rift Valley fever outbreak.

In climate science, an anomaly is the deviation of a quantity from its annual cycle. There are many ways to define annual cycle. Traditionally, this annual cycle is taken to be an exact repeat of itself year after year. This stationary annual cycle may not reflect well the intrinsic nonlinearity of the climate system, especially under external forcing. In this paper, we re-examine the reference frame for anomalies by re-examining the annual cycle. We propose an alternative reference frame for climate anomalies, the modulated annual cycle (MAC) that allows the annual cycle to change from year to year, for defining anomalies. In order for this alternative reference frame to be useful, we need to be able to define the instantaneous annual cycle: we therefore also introduce a new method to extract the MAC from climatic data. In the presence of a MAC, modulated in both amplitude and frequency, we can then define an alternative version of an anomaly, this time with respect to the instantaneous MAC rather than a permanent and unchanging AC. Based on this alternative definition of anomalies, we re-examine some familiar physical processes: in particular SST re-emergence and ENSO phase locking to the annual cycle. We find that the re-emergence mechanism may be alternatively interpreted as an explanation of the change of the annual cycle instead of an explanation of the interannual to interdecadal persistence of SST anomalies. We also find that the ENSO phase locking can largely be attributed to the residual annual cycle (the difference of the MAC and the corresponding traditional annual cycle) contained in the traditional anomaly, and, therefore, can be alternatively interpreted as a part of the annual cycle phase locked to the annual cycle itself. In addition to the examples of reinterpretation of physics of well known climate phenomena, we also present an example of the implications of using a MAC against which to define anomalies. We show that using MAC as a reference framework for anomaly can bypass the difficulty brought by concepts such as "decadal variability of summer (or winter) climate" for understanding the low-frequency variability of the climate system. The concept of an amplitude and frequency modulated annual cycle, a method to extract it, and its implications for the interpretation of physical processes, all may contribute potentially to a more consistent and fruitful way of examining past and future climate variability and change.

No abstract is provided for this article.

We use data from two satellites and a terrestrial carbon model to quantify the impact of urbanization on the carbon cycle and food production in the US as a result of reduced net primary productivity (NPP). Our results show that urbanization is taking place on the most fertile lands and hence has a disproportionately large overall negative impact on NPP. Urban land transformation in the US has reduced the amount of carbon fixed through photosynthesis by 0.04 pg per year or 1.6% of the pre-urban input. The reduction is enough to offset the 1.8% gain made by the conversion of land to agricultural use, even though urbanization covers an area less than 3% of the land surface in the US and agricultural lands approach 29% of the total land area. At local and regional scales, urbanization increases NPP in resource-limited regions and through localized warming “urban heat” contributes to the extension of the growing season in cold regions. In terms of biologically available energy, the loss of NPP due to urbanization of agricultural lands alone is equivalent to the caloric requirement of 16.5 million people, or about 6% of the US population.