Vegetation expansion in the subnival Hindu Kush Himalaya

Anderson, Karen; Fawcett, Dominic; Anthony, Cugulliere; Benford, Sophie; Jones, Darren; Leng, Ruolin

doi:10.1111/gcb.14919

Cited by 121 publications

(77 citation statements)

References 93 publications

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“…Based on proxy data, it has been inferred that high-latitude vegetation was distinctly different from modern in the mid-Pliocene (Salzmann et al, 2008a, b). Ballantyne et al (2006) show that trees were present over northern Canada and Greenland. With COSMOS we have been able to simulate mid-Pliocene vegetation changes for the first time in PlioMIP2.…”

Section: Shifts In Vegetation Patterns In the Mid-pliocene And The Fumentioning

confidence: 96%

Contribution of the coupled atmosphere–ocean–sea ice–vegetation model COSMOS to the PlioMIP2

et al. 2020

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Abstract. We present the Alfred Wegener Institute's contribution to the Pliocene Model Intercomparison Project Phase 2 (PlioMIP2) wherein we employ the Community Earth System Models (COSMOS) that include a dynamic vegetation scheme. This work builds on our contribution to Phase 1 of the Pliocene Model Intercomparison Project (PlioMIP1) wherein we employed the same model without dynamic vegetation. Our input to the PlioMIP2 special issue of Climate of the Past is twofold. In an accompanying paper we compare results derived with COSMOS in the framework of PlioMIP2 and PlioMIP1. With this paper we present details of our contribution with COSMOS to PlioMIP2. We provide a description of the model and of methods employed to transfer reconstructed mid-Pliocene geography, as provided by the Pliocene Reconstruction and Synoptic Mapping Initiative Phase 4 (PRISM4), to model boundary conditions. We describe the spin-up procedure for creating the COSMOS PlioMIP2 simulation ensemble and present large-scale climate patterns of the COSMOS PlioMIP2 mid-Pliocene core simulation. Furthermore, we quantify the contribution of individual components of PRISM4 boundary conditions to characteristics of simulated mid-Pliocene climate and discuss implications for anthropogenic warming. When exposed to PRISM4 boundary conditions, COSMOS provides insight into a mid-Pliocene climate that is characterised by increased rainfall (+0.17 mm d−1) and elevated surface temperature (+3.37 ∘C) in comparison to the pre-industrial (PI). About two-thirds of the mid-Pliocene core temperature anomaly can be directly attributed to carbon dioxide that is elevated with respect to PI. The contribution of topography and ice sheets to mid-Pliocene warmth is much smaller in contrast – about one-quarter and one-eighth, respectively, and nonlinearities are negligible. The simulated mid-Pliocene climate comprises pronounced polar amplification, a reduced meridional temperature gradient, a northwards-shifted tropical rain belt, an Arctic Ocean that is nearly free of sea ice during boreal summer, and muted seasonality at Northern Hemisphere high latitudes. Simulated mid-Pliocene precipitation patterns are defined by both carbon dioxide and PRISM4 paleogeography. Our COSMOS simulations confirm long-standing characteristics of the mid-Pliocene Earth system, among these increased meridional volume transport in the Atlantic Ocean, an extended and intensified equatorial warm pool, and pronounced poleward expansion of vegetation cover. By means of a comparison of our results to a reconstruction of the sea surface temperature (SST) of the mid-Pliocene we find that COSMOS reproduces reconstructed SST best if exposed to a carbon dioxide concentration of 400 ppmv. In the Atlantic to Arctic Ocean the simulated mid-Pliocene core climate state is too cold in comparison to the SST reconstruction. The discord can be mitigated to some extent by increasing carbon dioxide that causes increased mismatch between the model and reconstruction in other regions.

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Section: Shifts In Vegetation Patterns In the Mid-pliocene And The Fumentioning

confidence: 96%

Contribution of the coupled atmosphere–ocean–sea ice–vegetation model COSMOS to the PlioMIP2

et al. 2020

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“…In particular, the existence of several vegetation indices in GEE allows conducting vegetation studies in efficient and quick manners. GEE has been widely used for vegetation mapping [57], [58], vegetation dynamics monitoring [59], [60], deforestation [61], [62], vegetation and forest expansion [63], [64], forest health monitoring [65], [66], forest mapping [67], [68], pasture monitoring [49], [69], and rangeland assessment [70], [71]. For instance, the full archive of the Landsat imagery was processed within GEE to map the vegetation dynamics from 1988 to 2017 in Queensland, Australia [59].…”

Section: A Vegetationmentioning

confidence: 99%

Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review

Amani¹,

Ghorbanian

Ahmadi

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

651

216

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Remote Sensing (RS) systems have been collecting massive volumes of datasets for decades, managing and analyzing of which are not practical using common software packages and desktop computing resources. In this regard, Google has developed a cloud computing platform, called Google Earth Engine (GEE), to effectively address the challenges of big data analysis. In particular, this platform facilitates processing big geo data over large areas and monitoring the environment for long periods of time. Although this platform was launched in 2010 and has proved its high potential for different applications, it has not been fully investigated and utilized for RS applications until recent years. Therefore, this study aims to comprehensively explore different aspects of the GEE platform, including its datasets, functions, advantages/limitations, and various applications. For this purpose, 450 journal articles published in 150 journals between January 2010 and May 2020 were studied. It was observed that Landsat and Sentinel datasets were extensively utilized by GEE users. Moreover, supervised machine learning algorithms, such as Random Forest (RF), were more widely applied to image classification tasks. GEE has also been employed in a broad range of applications, such as Land Cover/land Use (LCLU) classification, hydrology, urban planning, natural disaster, climate analyses, and image processing. It was generally observed that the number of GEE publications has significantly increased during the past few years, and it is expected that GEE will be utilized by more users from different fields to resolve their big data processing challenges.

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“…With (2017) find that specific tree types will be able to migrate towards higher elevations of the Himalayas. Furthermore, based on recent satellite observations, Anderson et al (2020) find that vegetation is already expanding across the Himalayas. These results are confirmed by our simulations, and the agreement is larger the higher the prescribed level of carbon dioxide.…”

Section: Shifts In Vegetation Patterns In Mid-pliocene and Futurementioning

confidence: 99%

Contribution of the coupled atmosphere–ocean–sea ice–vegetation model COSMOS to the PlioMIP2

Stepanek¹,

Samakinwa²,

Lohmann³

2020

Preprint

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Abstract. We present the Alfred Wegener Institute's contribution to the Pliocene Model Intercomparison Project, Phase 2 (PlioMIP2) where we employ the Community Earth System Models (COSMOS) that include a dynamic vegetation scheme. This work builds on our contribution to Phase 1 of the Pliocene Model Intercomparison Project (PlioMIP1) where we employed the same model without dynamic vegetation. Our input to the PlioMIP2 special issue of Climate of the Past is twofold. In an accompanying manuscript we compare results derived with the COSMOS in the framework of PlioMIP2 and PlioMIP1. With the manuscript on hand we present details of our contribution with COSMOS to PlioMIP2. We provide a description of the model and of methods employed to transfer reconstructed Mid-Pliocene geography, as provided by the Pliocene Reconstruction and Synoptic Mapping Initiative, Phase 4 (PRISM4), to model boundary conditions. We describe the spin-up procedure for creating the COSMOS PlioMIP2 simulation ensemble and present large scale climate patterns of the COSMOS PlioMIP2 Mid-Pliocene core simulation. Furthermore, we quantify the contribution of individual components of PRISM4 boundary conditions to characteristics of simulated Mid-Pliocene climate and discuss implications for anthropogenic warming. When exposed to PRISM4 boundary conditions, the COSMOS provide insight into a Mid-Pliocene climate that is characterized by increased rainfall (+0.17 mm d−1) and elevated surface temperature (+3.37 °C) in comparison to the Pre-Industrial (PI). About two-thirds of the Mid-Pliocene core temperature anomaly can be directly attributed to carbon dioxide that is elevated w.r.t. PI. The contribution of topography and ice sheets to Mid-Pliocene warmth is in contrast much smaller – about one-fourth and one-eighth, respectively. The simulated Mid-Pliocene climate comprises pronounced polar amplification, a reduced meridional temperature gradient, a northward shifted tropical rain belt, an Arctic Ocean that is nearly free of sea ice during boreal summer, as well as muted seasonality in Northern Hemisphere high latitudes. Simulated Mid-Pliocene precipitation patterns are defined by both carbon dioxide and PRISM4 paleogeography. The COSMOS confirm longstanding characteristics of the Mid-Pliocene Earth System, among these increased meridional volume transport in the Atlantic Ocean, an extended and intensified equatorial warm pool, as well as pronounced poleward expansion of vegetation cover. By means of a comparison of our results to a reconstruction of sea surface temperature (SST) of the Mid-Pliocene we find that the COSMOS reproduce reconstructed SST best if exposed to a carbon dioxide concentration of 400 ppmv. In the Atlantic to Arctic Ocean the simulated Mid-Pliocene core climate state is too cold in comparison to the SST reconstruction. The discord can be mitigated to some extent by increasing carbon dioxide that causes increased mismatch between model and reconstruction in other regions.

show abstract

Vegetation expansion in the subnival Hindu Kush Himalaya

Cited by 121 publications

References 93 publications

Contribution of the coupled atmosphere–ocean–sea ice–vegetation model COSMOS to the PlioMIP2

Contribution of the coupled atmosphere–ocean–sea ice–vegetation model COSMOS to the PlioMIP2

Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review

Contribution of the coupled atmosphere–ocean–sea ice–vegetation model COSMOS to the PlioMIP2

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