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No abstract is provided for this article.

No abstract is provided for this article.

No abstract is provided for this article.

This paper reports on the analysis of Pathfinder AVHRR land (PAL) data set that spans the period July 1981 to September 1994. The time series of normalized difference vegetation index (NDVI) data for land areas north of 45/spl deg/ N assembled by correcting the PAL data with spectral methods confirms the northerly greening trend and extension of the photosynthetically active growing season. Analysis of the channel reflectance data indicates that the interannual changes in red and near-infrared reflectances are similar to seasonal changes in the spring time period when green leaf area increases and photosynthetic activity ramps up. Model calculations and theoretical analysis of the sensitivity of NDVI to background reflectance variations confirm the hypothesis that warming driven reductions in snow cover extent and earlier onset of greening are responsible for the observed changes in spectral reflectances over vegetated land areas.

Crop yield forecasting is performed monthly during the growing season by the United States Department of Agriculture’s National Agricultural Statistics Service. The underpinnings are long-established probability surveys reliant on farmers’ feedback in parallel with biophysical measurements. Over the last decade though, satellite imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS) has been used to corroborate the survey information. This is facilitated through the Global Inventory Modeling and Mapping Studies/Global Agricultural Monitoring system, which provides open access to pertinent real-time normalized difference vegetation index (NDVI) data. Hence, two relatively straightforward MODIS-based modeling methods are employed operationally. The first model constitutes mid-season timing based on the maximum peak NDVI value, while the second is reflective of late-season timing by integrating accumulated NDVI over a threshold value. Corn model results nationally show the peak NDVI method provides a R2 of 0.88 and a coefficient of variation (CV) of 3.5%. The accumulated method, using an optimally derived 0.58 NDVI threshold, improves the performance to 0.93 and 2.7%, respectively. Both these models outperform simple trend analysis, which is 0.48 and 7.4%, correspondingly. For soybeans the R2 results of the peak NDVI model are 0.62, and 0.73 for the accumulated using a 0.56 threshold. CVs are 6.8% and 5.7%, respectively. Spring wheat’s R2 performance with the accumulated NDVI model is 0.60 but just 0.40 with peak NDVI. The soybean and spring wheat models perform similarly to trend analysis. Winter wheat and upland cotton show poor model performance, regardless of method. Ultimately, corn yield forecasting derived from MODIS imagery is robust, and there are circumstances when forecasts for soybeans and spring wheat have merit too.

The amplitude of the CO(2) seasonal cycle at the Mauna Loa Observatory (MLO) increased from the early 1970s to the early 1990s but decreased thereafter despite continued warming over northern continents. Because of its location relative to the large-scale atmospheric circulation, the MLO receives mainly Eurasian air masses in the northern hemisphere (NH) winter but relatively more North American air masses in NH summer. Consistent with this seasonal footprint, our findings indicate that the MLO amplitude registers North American net carbon uptake during the warm season and Eurasian net carbon release as well as anomalies in atmospheric circulation during the cold season. From the early 1970s to the early 1990s, our analysis was consistent with that of Keeling et al. [Keeling CD, Chin JFS, Whorf TP (1996) Nature 382:146-149], suggesting that the increase in the MLO CO(2) amplitude is dominated by enhanced photosynthetic drawdown in North America and enhanced respiration in Eurasia. In contrast, the recent decline in the CO(2) amplitude is attributed to reductions in carbon sequestration over North America associated with severe droughts from 1998 to 2003 and changes in atmospheric circulation leading to decreased influence of Eurasian air masses. With the return of rains to the U.S. in 2004, both the normalized difference vegetation index and the MLO amplitude sharply increased, suggesting a return of the North American carbon sink to more normal levels. These findings indicate that atmospheric CO(2) measurements at remote sites can continue to play an important role in documenting changes in land carbon flux, including those related to widespread drought, which may continue to worsen as a result of global warming.

This paper analyzes the relation between satellite‐based measures of vegetation greenness and climate by land cover type at a regional scale (2° × 2° grid boxes) between 1982 and 1999. We use the normalized difference vegetation index (NDVI) from the Global Inventory Monitoring and Modeling Studies (GIMMS) data set to quantify climate‐induced changes in terrestrial vegetation. Climatic conditions are represented with monthly data for land surface air temperature and precipitation. The relation between NDVI and the climate variables is represented using a quadratic specification, which is consistent with the notion of a physiological optimum. The effects of spatial heterogeneity and unobserved variables are estimated with specifications and statistical techniques that allow coefficients to vary among grid boxes. Using this methodology, we are able to estimate statistically meaningful relations between NDVI and climate during spring, summer, and autumn for forests between 40°N and 70°N in North America and Eurasia. Of the variables examined, changes in temperature account for the largest fraction of the change in NDVI between the early 1980s and the late 1990s. Changes in stratospheric aerosol optical depth and precipitation have a smaller effect, while artifacts associated with variations in solar zenith angle are negligible. These results indicate that temperature changes between the early 1980s and the late 1990s are responsible for much of the observed increase in satellite measures of northern forest greenness.

An increase in photosynthetic activity of the northern hemisphere terrestrial vegetation, as derived from satellite observations, has been reported in previous studies. The amplitude of the seasonal cycle of the annually detrended atmospheric CO 2 in the northern hemisphere (an indicator of biospheric activity) also increased during that period. We found, by analyzing the annually detrended CO 2 record by season, that early summer (June) CO 2 concentrations indeed decreased from 1985 to 1991, and they have continued to decrease from 1994 up to 2002. This decrease indicates accelerating springtime net CO 2 uptake. However, the CO 2 minimum concentration in late summer (an indicator of net growing-season uptake) showed no positive trend since 1994, indicating that lower net CO 2 uptake during summer cancelled out the enhanced uptake during spring. Using a recent satellite normalized difference vegetation index data set and climate data, we show that this lower summer uptake is probably the result of hotter and drier summers in both mid and high latitudes, demonstrating that a warming climate does not necessarily lead to higher CO 2 growing-season uptake, even in high-latitude ecosystems that are considered to be temperature limited.

We used a new 17‐year, high spatial resolution satellite record and a carbon cycle model to explore how changing net primary productivity (NPP) contributed to a proposed carbon (C) sink in North America. We found a small but significant increase in NPP, 0.03 Pg C yr −2 or 8% over 17 years, that could explain a substantial fraction of the C sink. The largest increases occurred in the central and southeastern United States, eastern Canada, and northwestern North America, and were consistent with NPP trends derived from forest inventories and crop yields. Interannual NPP variability was small, implying that the large interannual variability in the C sink found in previous studies were driven by changes in heterotrophic respiration.

Climate models participating in the IPCC Fourth Assessment Report indicate that under a 2xCO2 environment, runoff would increase faster than precipitation overland. However, observations over large U.S watersheds indicate otherwise. This inconsistency suggests that there may be important feedbacks between climate and land surface unaccounted for in the present generation of models. We postulate that the increase in precipitation associated with the increase in CO2 is also increasing vegetation density, which may already be feeding back onto climate. Including this feedback in a climate model simulation resulted in precipitation and runoff trends consistent with observations and reduced the warming by 0.6OC overland. This unaccounted for missing water may be linked to about 10% of the missing land carbon sink. A recent compilation of outputs from 19 coupled atmosphere-ocean general circulation models used in the IPCC Fourth Assessment Report (AR4) shows projected increases in air temperature, precipitation and river discharge for 24 major rivers in the world in response to doubling CO2 by the end of the century (1). The ensemble mean from these models also indicates that, compared to their respective baselines overland, the global mean of the runoff change would increase faster (8.9% per year) than that of the precipitation (5% per year). We analyze century-scale observed annual runoff time-series (1901-2002) over 9 hydrological units covering large regions of the Eastern United States (Fig.1) compiled by the United States Geological Survey (USGS)(2). These regions were selected because they are the most forested; the least water-limited and are not under extensive irrigation. We compare these time-series to similar time-series of observed annual precipitation anomalies spanning the period 1900-1995 (3). Both time-series exhibit a positive longterm trend (Fig. 2); however, in contrast to the analysis of (I), these historic data records show that the rate of precipitation increase is 5.5 % per year, roughly double the rate of runoff increase of 3.1 % per year.