Coupled vegetation‐precipitation variability observed from satellite and climate records

Lotsch, Alexander; Friedl, M. A.; Anderson, Bruce T.; Tucker, Compton J.

doi:10.1029/2003gl017506

Cited by 173 publications

(121 citation statements)

References 25 publications

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“…To represent anomalies in moisture supply, we computed a SPI (29), defined as the precipitation anomaly over a specified time period leading up to and including the month of interest normalized by its SD. For this study, we computed a 6-month SPI (SPI6) for each month on the basis of the 1959-2004 precipitation record, because the variability in moisture supply at this time scale compares well with variations in seasonal vegetation productivity (30).…”

Section: Methodsmentioning

confidence: 99%

The changing carbon cycle at Mauna Loa Observatory

Buermann¹,

Lintner

Koven³

et al. 2007

Proc. Natl. Acad. Sci. U.S.A.

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113

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The amplitude of the CO2 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 ( atmospheric circulation ͉ atmospheric CO2 seasonal cycle ͉ terrestrial carbon sinks ͉ continental droughts T he time series of CO 2 at Mauna Loa Observatory (MLO) located on the Island of Hawaii is unique not only because of its accuracy and length but also because it was designed and has been repeatedly demonstrated to capture the globally averaged secular trend in atmospheric CO 2 . The seasonal cycle of atmospheric CO 2 at the MLO, with a maximum at the beginning of the growing season (May) and a minimum at the end of the growing season (September/October), records the ''breathing'' of the northern hemisphere (NH) biosphere, that is, the seasonal asynchrony between photosynthetic drawdown and respiratory release of CO 2 by terrestrial ecosystems (e.g., refs. 1-3).During the course of a year, the MLO experiences marked shifts in large-scale atmospheric circulation. In the NH cold season, air masses from Eurasia dominate transport to the MLO as a result of a deepening of the Aleutian Low and intensified midlatitude westerly flow (4). In the NH warm season, the dominant subtropical North Pacific high-pressure system located to the northeast of the MLO leads to short-range transport of air masses to the MLO that originate over or near the North American continent (4). During this season, the MLO receives an approximately equal mix of air masses from both continents with relatively more North American contributions at the peak of the NH growing season (July/August). The MLO is also located in the vicinity of the subsiding branch of the Hadley circulation, rendering it sensitive to interhemispheric mixing.Several studies have analyzed variability in the MLO CO 2 seasonal cycle to infer the sensitivity of ecosystem dynamics to climate perturbations. The increasing trends in the seasonal amplitude of CO 2 at the MLO and Point Barrow, AK, from the early 1970s to the early 1990s are postulated to be evidence of a temperature-related lengthening of the boreal growing season (1

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Section: Methodsmentioning

confidence: 99%

The changing carbon cycle at Mauna Loa Observatory

Buermann¹,

Lintner

Koven³

et al. 2007

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

113

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“…In addition, it allows for the detection of double cropped regions such as can be found in Eastern Africa. Additional information on growing season dynamics in Africa can be found in the literature in a variety of disciplines (Ellsworth & Shapiro, 1989;Justice et al, 1985;Lotsch et al, 2003;Sacks et al, in press;Verdin et al, 1999).…”

Section: Phenological Analysismentioning

confidence: 99%

The response of African land surface phenology to large scale climate oscillations

Brown

Beurs

Vrieling

2010

Remote Sensing of Environment

129

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Variations in agricultural production due to rainfall and temperature fluctuations are a primary cause of food insecurity on the African continent. Analysis of changes in phenology can provide quantitative information on the effect of climate variability on growing seasons in agricultural regions. Using a robust statistical methodology, we describe the relationship between phenology metrics derived from the 26 year AVHRR NDVI record and the North Atlantic Oscillation index (NAO), the Indian Ocean Dipole (IOD), the Pacific Decadal Oscillation (PDO), and the Multivariate ENSO Index (MEI). We map the most significant positive and negative correlation for the four climate indices in Eastern, Western and Southern Africa between two phenological metrics and the climate indices. Our objective is to provide evidence of whether climate variability captured in the four indices has had a significant impact on the vegetative productivity of Africa during the past quarter century. We found that the start of season and cumulative NDVI were significantly affected by large scale variations in climate. The particular climate index and the timing showing highest correlation depended heavily on the region examined. In Western Africa the cumulative NDVI correlates with PDO in September-November. In Eastern Africa the start of the June-October season strongly correlates with PDO in March-May, while the PDO in December-February correlates with the start of the FebruaryJune season. The cumulative NDVI over this last season relates to the MEI of March-May. For Southern Africa, high correlations exist between SOS and NAO of September-November, and cumulative NDVI and MEI of March-May. The research shows that climate indices can be used to anticipate late start and variable vigor in the growing season of sensitive agricultural regions in Africa.Published by Elsevier Inc.

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“…In our study, the NPP in Northeast China most significantly responded to long timescale SPEI values (6 months and 12 months). In the US Great Plains, Ji and Peters (2003) used the SPI (Standardized Precipitation Index) to quantify the effects of droughts on the NDVI and found that the 3 month SPI was most strongly correlated with the NDVI during the midpoint of the growing season, while Lotsch et al (2003) suggested the 5 month SPI best assessed the response of terrestrial ecosystems to droughts. Liu et al (2014) also reported that Chinese semiarid and sub-humid ecosystems responded to droughts over long timescales, and this was consistent with our results.…”

Section: Lag Effect Of Droughts On Nppmentioning

confidence: 99%

Effects of drought on net primary productivity: Roles of temperature, drought intensity, and duration

Sun¹,

Zhao²,

Wang

2016

Chin. Geogr. Sci.

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Northeast China has experienced frequent droughts over the past fifteen years. However, the effects of droughts on net primary productivity (NPP) in Northeast China remain unclear. In this paper, the droughts that occurred in Northeast China between 1999 and 2013 were identified using the Standardized Precipitation Evapotranspiration Index (SPEI). The NPP standardized anomaly index (NPP-SAI) was used to evaluate NPP anomalies. The years of 1999, 2000, 2001, and 2007 were further investigated in order to explore the influence of droughts on NPP at different time scales (3, 6, and 12 months). Based on the NPP-SAI of normal areas, we found droughts overall decreased NPP by 112.06 Tg C between 1999 and 2013. Lower temperatures at the beginning of the growing season could cause declines in NPP by shortening the length of the growing season. Mild drought or short-term drought with higher temperatures might increase NPP, and weak intensity droughts intensified the lag effects of droughts on NPP.

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Coupled vegetation‐precipitation variability observed from satellite and climate records

Cited by 173 publications

References 25 publications

The changing carbon cycle at Mauna Loa Observatory

The changing carbon cycle at Mauna Loa Observatory

The response of African land surface phenology to large scale climate oscillations

Effects of drought on net primary productivity: Roles of temperature, drought intensity, and duration

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