An atmosphere–ocean time series model of global climate change

David I. Stern

doi:10.1016/j.csda.2005.09.016

Modelling the productivity of Siberian larch forests from Landsat NDVI time series in fragmented forest stands of the Mongolian forest-steppe

Environmental Monitoring and Assessment ◽

10.1007/s10661-021-08996-1 ◽

2021 ◽

Vol 193 (4) ◽

Author(s):

Stefan Erasmi ◽

Michael Klinge ◽

Choimaa Dulamsuren ◽

Florian Schneider ◽

Markus Hauck

Keyword(s):

Climate Change ◽

Time Series ◽

Global Climate Change ◽

Global Climate ◽

Siberian Larch ◽

Growing Season ◽

Forest Productivity ◽

Forest Steppe ◽

Fragmented Forest ◽

Southern Fringe

AbstractThe monitoring of the spatial and temporal dynamics of vegetation productivity is important in the context of carbon sequestration by terrestrial ecosystems from the atmosphere. The accessibility of the full archive of medium-resolution earth observation data for multiple decades dramatically improved the potential of remote sensing to support global climate change and terrestrial carbon cycle studies. We investigated a dense time series of multi-sensor Landsat Normalized Difference Vegetation Index (NDVI) data at the southern fringe of the boreal forests in the Mongolian forest-steppe with regard to the ability to capture the annual variability in radial stemwood increment and thus forest productivity. Forest productivity was assessed from dendrochronological series of Siberian larch (Larix sibirica) from 15 plots in forest patches of different ages and stand sizes. The results revealed a strong correlation between the maximum growing season NDVI of forest sites and tree ring width over an observation period of 20 years. This relationship was independent of the forest stand size and of the landscape’s forest-to-grassland ratio. We conclude from the consistent findings of our case study that the maximum growing season NDVI can be used for retrospective modelling of forest productivity over larger areas. The usefulness of grassland NDVI as a proxy for forest NDVI to monitor forest productivity in semi-arid areas could only partially be confirmed. Spatial and temporal inconsistencies between forest and grassland NDVI are a consequence of different physiological and ecological vegetation properties. Due to coarse spatial resolution of available satellite data, previous studies were not able to account for small-scaled land-cover patches like fragmented forest in the forest-steppe. Landsat satellite-time series were able to separate those effects and thus may contribute to a better understanding of the impact of global climate change on natural ecosystems.

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Detection of anthropogenic climate change in satellite records of ocean chlorophyll and productivity

Biogeosciences ◽

10.5194/bg-7-621-2010 ◽

2010 ◽

Vol 7 (2) ◽

pp. 621-640 ◽

Cited By ~ 258

Author(s):

S. A. Henson ◽

J. L. Sarmiento ◽

J. P. Dunne ◽

L. Bopp ◽

I. Lima ◽

...

Keyword(s):

Climate Change ◽

Time Series ◽

Primary Production ◽

Global Climate Change ◽

Decadal Variability ◽

Global Climate ◽

Warming Trend ◽

Ocean Colour ◽

Ocean Productivity ◽

The Impact

Abstract. Global climate change is predicted to alter the ocean's biological productivity. But how will we recognise the impacts of climate change on ocean productivity? The most comprehensive information available on its global distribution comes from satellite ocean colour data. Now that over ten years of satellite-derived chlorophyll and productivity data have accumulated, can we begin to detect and attribute climate change-driven trends in productivity? Here we compare recent trends in satellite ocean colour data to longer-term time series from three biogeochemical models (GFDL, IPSL and NCAR). We find that detection of climate change-driven trends in the satellite data is confounded by the relatively short time series and large interannual and decadal variability in productivity. Thus, recent observed changes in chlorophyll, primary production and the size of the oligotrophic gyres cannot be unequivocally attributed to the impact of global climate change. Instead, our analyses suggest that a time series of ~40 years length is needed to distinguish a global warming trend from natural variability. In some regions, notably equatorial regions, detection times are predicted to be shorter (~20–30 years). Analysis of modelled chlorophyll and primary production from 2001–2100 suggests that, on average, the climate change-driven trend will not be unambiguously separable from decadal variability until ~2055. Because the magnitude of natural variability in chlorophyll and primary production is larger than, or similar to, the global warming trend, a consistent, decades-long data record must be established if the impact of climate change on ocean productivity is to be definitively detected.

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Global climate change and North American mammalian evolution

Paleobiology ◽

10.1017/s0094837300026968 ◽

2000 ◽

Vol 26 (S4) ◽

pp. 259-288 ◽

Cited By ~ 9

Author(s):

John Alroy ◽

Paul L. Koch ◽

James C. Zachos

Keyword(s):

Climate Change ◽

Time Series ◽

Oxygen Isotope ◽

Body Mass ◽

Global Climate Change ◽

North American ◽

Global Climate ◽

Isotope Ratios ◽

Mammalian Evolution ◽

Oxygen Isotope Ratios

We compare refined data sets for Atlantic benthic foraminiferal oxygen isotope ratios and for North American mammalian diversity, faunal turnover, and body mass distributions. Each data set spans the late Paleocene through Pleistocene and has temporal resolution of 1.0 m.y.; the mammal data are restricted to western North America. We use the isotope data to compute five separate time series: oxygen isotope ratios at the midpoint of each 1.0-m.y. bin; changes in these ratios across bins; absolute values of these changes (= isotopic volatility); standard deviations of multiple isotope measurements within each bin; and standard deviations that have been detrended and corrected for serial correlation. For the mammals, we compute 12 different variables: standing diversity at the start of each bin; per-lineage origination and extinction rates; total turnover; net diversification; the absolute value of net diversification (= diversification volatility); change in proportional representation of major orders, as measured by a simple index and by a G-statistic; and the mean, standard deviation, skewness, and kurtosis of body mass. Simple and liberal statistical analyses fail to show any consistent relationship between any two isotope and mammalian time series, other than some unavoidable correlations between a few untransformed, highly autocorrelated time series like the raw isotope and mean body mass curves. Standard methods of detrending and differencing remove these correlations. Some of the major climate shifts indicated by oxygen isotope records do correspond to major ecological and evolutionary transitions in the mammalian biota, but the nature of these correspondences is unpredictable, and several other such transitions occur at times of relatively little global climate change. We conclude that given currently available climate records, we cannot show that the impact of climate change on the broad patterns of mammalian evolution involves linear forcings; instead, we see only the relatively unpredictable effects of a few major events. Over the scale of the whole Cenozoic, intrinsic, biotic factors like logistic diversity dynamics and within-lineage evolutionary trends seem to be far more important.

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Time Series Properties of Some Climatic Variables in Dinajpur District

Journal of Environmental Science and Natural Resources ◽

10.3329/jesnr.v6i2.22109 ◽

2015 ◽

Vol 6 (2) ◽

pp. 127-137

Author(s):

MJ Rahman ◽

JA Syeda ◽

M Nasser

Keyword(s):

Climate Change ◽

Time Series ◽

Natural Resources ◽

Climatic Change ◽

Global Climate Change ◽

Global Climate ◽

Unit Roots ◽

Cointegration Analysis ◽

Statistical Tools ◽

Local Climate

Several direct empirical time series investigations of global climate change and its impact have been studied by several world famous researchers. Some researches regarding local climatic change and its impact have been published but the time series properties of the variables related to national as well as local climate are yet to be able to have proper attention. The presence or absence of unit roots in these time series or inappropriate statistical tools may challenge the validity of the interpretations of their results and implies that cointegration analysis can be used to investigate the relation among variables. This article attempts to deduce time series properties of temperature, rainfall and humidity of Dinajpur district.DOI: http://dx.doi.org/10.3329/jesnr.v6i2.22109 J. Environ. Sci. & Natural Resources, 6(2): 127-137 2013

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