scholarly journals Scenario Changes of Atlantic Water in the Arctic Ocean

2015 ◽  
Vol 28 (14) ◽  
pp. 5523-5548 ◽  
Author(s):  
Zhenxia Long ◽  
Will Perrie

Abstract The authors explore possible temperature modifications of the Atlantic Water Layer (AWL) induced by climate change, performing simulations for 1970 to 2099 with a coupled ice–ocean Arctic model (CIOM). Surface fields to drive the CIOM were provided by the Canadian Regional Climate Model (CRCM), driven by outputs from the Canadian Centre for Climate Modelling and Analysis (CCCma) Coupled Global Climate Model, version 3 (CGCM3) following the A1B climate change scenario. In the present climate, represented as 1990–2009, the CIOM can reliably reproduce the AWL compared to Polar Science Center Hydrographic Climatology (PHC) data. For the future climate, assuming the A1B climate change scenario, there is a significant increase in water volume transport into the central Arctic Ocean through Fram Strait due to the weakened atmospheric high pressure system over the western Arctic and an intensified atmospheric low pressure system over the Nordic seas. The AWL temperature tends to decrease from 0.36°C in the 2010s to 0.26°C in the 2060s. In the vertical, the warm Atlantic water core slightly expands before the 2030s, significantly shrinks after the 2050s, and essentially disappears by 2070–99, in the southern Beaufort Sea. The temperature decrease after 2030 is mainly due to the reduced heat fluxes in the Kara and Barents Seas. In the northeastern Barents and Kara Seas, the loss of sea ice increases the heat loss from the Atlantic water and reduces the water temperature near the bottom, contributing to decreased heat fluxes into the central Arctic Ocean, as well as decreased AWL temperature at central Arctic Ocean intermediate layers. In addition, the vertically integrated heat loss also plays an important role in the AWL cooling process.

2020 ◽  
Author(s):  
Yong-Tak Kim ◽  
Carlos H R Lima ◽  
Hyun-Han Kwon

<p>Rainfall simulation by climate model is generally provided at coarse grids and bias correction is routinely needed for the hydrological applications. This study aims to explore an alternative approach to downscale daily rainfall simulated by the regional climate model (RCM) at any desired grid resolution along with bias correction using a Kriging model, which better represents spatial dependencies of distribution parameters across the watershed. The Kringing model also aims to reproduce the spatial variability observed in the ground rainfall gauge. The proposed model is validated through the entire weather stations in South Korea and climate change scenarios simulated by the five different RCMs informed by two GCMs. The results confirmed that the proposed spatial downscaling model could reproduce the observed rainfall statistics and spatial variability of rainfall. The proposed model further applied to the climate change scenario. A discussion of the potential uses of the mode is offered.</p><p>KEYWORDS: Climate Change Scenario, Global Climate Models, Regional Climate Models, Statistical Downscaling, Spatial-Temporal Bias</p><p> </p><p>Acknowledgement</p><p>This work was funded by the Korea Meteorological Administration Research and Development Program under Grant KMI(KMI2018-01215)</p>


Land ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 385
Author(s):  
Beatrice Nöldeke ◽  
Etti Winter ◽  
Yves Laumonier ◽  
Trifosa Simamora

In recent years, agroforestry has gained increasing attention as an option to simultaneously alleviate poverty, provide ecological benefits, and mitigate climate change. The present study simulates small-scale farmers’ agroforestry adoption decisions to investigate the consequences for livelihoods and the environment over time. To explore the interdependencies between agroforestry adoption, livelihoods, and the environment, an agent-based model adjusted to a case study area in rural Indonesia was implemented. Thereby, the model compares different scenarios, including a climate change scenario. The agroforestry system under investigation consists of an illipe (Shorea stenoptera) rubber (Hevea brasiliensis) mix, which are both locally valued tree species. The simulations reveal that farmers who adopt agroforestry diversify their livelihood portfolio while increasing income. Additionally, the model predicts environmental benefits: enhanced biodiversity and higher carbon sequestration in the landscape. The benefits of agroforestry for livelihoods and nature gain particular importance in the climate change scenario. The results therefore provide policy-makers and practitioners with insights into the dynamic economic and environmental advantages of promoting agroforestry.


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