The impact of insolation, greenhouse gas forcing and ocean circulation changes on glacial inception

The Holocene ◽  
2011 ◽  
Vol 21 (5) ◽  
pp. 803-817 ◽  
Author(s):  
G. Vettoretti ◽  
W.R. Peltier
Keyword(s):  
Snow Cover ◽  
Greenhouse Gas ◽  
Ocean Circulation ◽  
High Latitude ◽  
Goodness Of Fit ◽  
Decadal Variability ◽  
Snow Accumulation ◽  
High Northern Latitude ◽  
The Future ◽  
The Impact

In this study we employ the NCAR CCSM3 coupled model to investigate the onset of high northern latitude perennial snow cover. Two periods of Earth’s insolation history, that of the pre-industrial period and that of 116 ka before present (BP), are used as benchmarks in an investigation of the influences of interglacial greenhouse gas (GHG) concentration and insolation upon the occurrence of permanent summer snow cover. An additional two experiments at 10 ka and 51 ka into the future (AP) using a typical interglacial GHG level are used to investigate the length of the current interglacial. Results from this set of multicentury sensitivity experiments demonstrate the relative importance of forcings due to insolation and atmospheric greenhouse gases at the millennial scale, and of Atlantic ocean overturning strength (AMOC) at the century scale. We find that while areas of perennial snow cover are sensitive to GHG concentrations, they are much more sensitive to the contemporaneous insolation regime. The goodness of fit of the climatology of the control model to the modern observed climatology is found to influence the modeling results. While there is a strong correlation between AMOC decadal variability and high latitude surface temperature in our control climates, we find little change in AMOC strength during our simulations of 116 ka BP climate nor do we find significant correlation between high latitude snow accumulation and the AMOC. Both the 10 ka AP and 51 ka AP future simulations produce inception events which are much stronger than that of the equivalent pre-industrial simulation. The simulation of inception at 10 ka into the future suggests a maximum duration of the current interglacial of approximately 20 ka in the absence of modern anthropogenic forcing.

The Opportunities and Challenges of Chinese Renewable Energy Greenhouse Gas Emission Reduction Trade

2014 ◽  
Vol 1010-1012 ◽  
pp. 2094-2101
Author(s):  
Long Xi Han ◽  
Jia Jia Zhai ◽  
Lin Zhang
Keyword(s):  
Renewable Energy ◽  
Greenhouse Gas ◽  
Emission Reduction ◽  
Greenhouse Gas Emission ◽  
Carbon Trading ◽  
Ghg Emission ◽  
Trading System ◽  
Trading Market ◽  
The Future ◽  
The Impact

The opportunities and challenges in the field of Chinese renewable energy were analyzed through the impact of global greenhouse gas (GHG) emission reduction trade, especially CDM on Chinese renewable energy, combined with the enhancement of awareness of voluntary emission reduction, relationship between emission reduction trade and renewable energy, changes in the international trade environment and the rise of the domestic trading system. It is suggested that the renewable energy industry integrates with GHG emission reduction trading system in China and explores the huge double benefit of emission reduction and income increase with market means, providing a reference for the smooth implementation of nationwide CN ETS including varies industries in the carbon trading market in the future, and striving for the speaking right for China to set the marketing price of international GHG emission reduction trading in the future.

ASSESSMENT OF CHANGES IN THE CHARACTERISTICS OF THE WATER, SNOW AND ICE REGIMES IN THE BASINS OF THE SMALL NORTHERN DVINA RIVER UNDER MODERN CONDITIONS AND THEIR POSSIBLE CHANGES IN THE FUTURE

2021 ◽  
pp. 117-127
Author(s):  
M. V. GEORGIEVSKY ◽  
◽  
N. I. GOROSHKOVA ◽  
V. A. KHOMYAKOVA ◽  
A. V. STRIZHENOK
Keyword(s):  
Snow Cover ◽  
Climate Warming ◽  
Snow Water Equivalent ◽  
Winter Precipitation ◽  
Spring Runoff ◽  
The Future ◽  
Snow Water ◽  
Project Data ◽  
The Impact ◽  
Slight Downward Trend

The article presents an analysis of the impact of climate change on the main characteristics of ice phenomena, snow cover and the water regime in the Small Northern Dvina River basin occurring in recent decades. Recently, a significant climate warming has been observed in the basin. As a result, winters are getting warmer and shorter. There is also an increase in winter precipitation and the number of thaws. Climate warming directly affects the duration of snow cover, which decreases both due to the later formation and to the earlier destruction of snow. There is also a slight downward trend in the annual values of the maximum snow water equivalent, which may be the result of an increase in the number of thaws in winter, when a part of the snow cover melts contributing to the winter river runoff. The analysis of the main characteristics of the ice cover on the rivers of the studied basin shows that their changes are similarly to changes in the snow cover: there is a reduction in the freeze-up period due to its later formation and earlier complete destruction. The maximum ice thickness on the rivers of the basin also tends to decrease. There is an increase in winter and a decrease in spring runoff. Predictive estimates of changes in the observed trends in the future are presented in the fi nal part of the article based on the CMIP5 project data.

scholarly journals Future Predictions of Rainfall and Temperature Using GCM and ANN for Arid Regions: A Case Study for the Qassim Region, Saudi Arabia

Water ◽  
2018 ◽  
Vol 10 (9) ◽  
pp. 1260 ◽  
Author(s):  
Khalid Alotaibi ◽  
Abdul Ghumman ◽  
Husnain Haider ◽  
Yousry Ghazaw ◽  
Md. Shafiquzzaman
Keyword(s):  
Saudi Arabia ◽  
Greenhouse Gas ◽  
Greenhouse Gas Emission ◽  
Data Driven ◽  
Emission Scenarios ◽  
Gas Emission ◽  
The Future ◽  
Emissions Scenarios ◽  
The Impact

Future predictions of rainfall patterns in water-scarce regions are highly important for effective water resource management. Global circulation models (GCMs) are commonly used to make such predictions, but these models are highly complex and expensive. Furthermore, their results are associated with uncertainties and variations for different GCMs for various greenhouse gas emission scenarios. Data-driven models including artificial neural networks (ANNs) and adaptive neuro fuzzy inference systems (ANFISs) can be used to predict long-term future changes in rainfall and temperature, which is a challenging task and has limitations including the impact of greenhouse gas emission scenarios. Therefore, in this research, results from various GCMs and data-driven models were investigated to study the changes in temperature and rainfall of the Qassim region in Saudi Arabia. Thirty years of monthly climatic data were used for trend analysis using Mann–Kendall test and simulating the changes in temperature and rainfall using three GCMs (namely, HADCM3, INCM3, and MPEH5) for the A1B, A2, and B1 emissions scenarios as well as two data-driven models (ANN: feed-forward-multilayer, perceptron and ANFIS) without the impact of any emissions scenario. The results of the GCM were downscaled for the Qassim region using the Long Ashton Research Station’s Weather Generator 5.5. The coefficient of determination (R2) and Akaike’s information criterion (AIC) were used to compare the performance of the models. Results showed that the ANNs could outperform the ANFIS for predicting long-term future temperature and rainfall with acceptable accuracy. All nine GCM predictions (three models with three emissions scenarios) differed significantly from one another. Overall, the future predictions showed that the temperatures of the Qassim region will increase with a specified pattern from 2011 to 2099, whereas the changes in rainfall will differ over various spans of the future.

Anoxic metabolism after the 21st century in oxygen minimum zones

2020 ◽  
Author(s):  
Wolfgang Koeve ◽  
Angela Landolfi
Keyword(s):  
Organic Matter ◽  
Ocean Circulation ◽  
Large Scale ◽  
Deep Ocean ◽  
Sulphate Reduction ◽  
Oxygen Minimum Zones ◽  
The Future ◽  
Ocean Models ◽  
The Impact ◽  
Oxygen Minimum

<p>Global models project a decrease of marine oxygen over the course of the 21th century. The future of marine oxygen becomes increasingly uncertain further into the future after yr 2100 , partly because ocean models differ in the way organic matter remineralisation continues under oxygen- and nitrate-free conditions. Using an Earth system model of intermediate complexity we found that under a business-as-usual CO2-emission scenario ocean deoxygenation further intensifies for several centuries until eventually ocean circulation re-establishes and marine oxygen increases again. (Oschlies et al. 2019, DOI 10.1038/s41467-019-10813-w).</p><p>In the Pacific Ocean the deoxygenation after yr 2100 goes along with the large scale loss of nitrate from oxygen minimum zones. Here we explore the impact on simulated ocean biogeochemistry of three different process formulation of anoxic metabolism, which have been used in other ocean models: (1) implicit sulphate reduction (organic matter degradation continues without oxidant), (2) no sulphidic metabolism (organic matter is not degraded under anoxic conditions), and (3) explicit sulphate reduction (with H2S as explicit model tracer). The model with explicit sulfphate reduction supports larger regional organic matter fluxed into the deep ocean and an increase in respired carbon storage, compared with the model applying implicit sulphate. We discuss the impact of anoxic metabolism on the coupling between export production and respired carbon stored in the ocean interior.</p>

scholarly journals Assessing the Sustainability of Ski Fields in Southern Japan under Global Warming

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Asuka Suzuki-Parker ◽  
Yoshika Miura ◽  
Hiroyuki Kusaka ◽  
Masaaki Kureha
Keyword(s):  
Climate Change ◽  
Field Survey ◽  
Dynamical Downscaling ◽  
Snow Accumulation ◽  
First Century ◽  
Future Projection ◽  
The Future ◽  
Southern Border ◽  
Natural Snow ◽  
The Impact

This is the first study in assessing the impact of climate change on Japanese ski fields with ensemble dynamical downscaling simulations. We target three ski fields in Ehime Prefecture, a southern border area for skiing in Japan. Our field survey revealed that a field located above 1200 m altitudes currently operates on natural snow supply, but those located at lower altitudes depend solely or partially on artificial snow supply. Fields are currently open for 82∼105 days. We analyzed ensemble high-resolution (5 km) dynamical downscaling simulations for future ski season durations with natural and artificial snow supplies. The future projection results for the end of the twenty-first century suggested that there would be virtually no natural snow accumulation in the study area for skiing. With artificial snow supply, a field located above 1200 m would be able to retain more than two months of ski season duration. Fields located at lower altitudes would only be able to open for 37∼43 days even with artificial snow supply. While the above projections suggest a severe outlook for the targeted ski fields, it is important to note that there is a strong demand from local skiers at beginner/intermediate levels for these ski fields. Thus, as long as these demands remain in the future, and if a business model to maximize profit during short opening periods is established, it may be possible to offset profit loss due to climate change.

scholarly journals Impact of Surface Albedo Assimilation on Snow Estimation

2020 ◽  
Vol 12 (4) ◽  
pp. 645 ◽  
Author(s):  
Sujay Kumar ◽  
David Mocko ◽  
Carrie Vuyovich ◽  
Christa Peters-Lidard
Keyword(s):  
Snow Cover ◽  
Land Surface ◽  
Snow Depth ◽  
Surface Albedo ◽  
Land Surface Model ◽  
Snow Accumulation ◽  
High Plains ◽  
Surface Model ◽  
Fractional Snow Cover ◽  
The Impact

Surface albedo has a significant impact in determining the amount of available net radiation at the surface and the evolution of surface water and energy budget components. The snow accumulation and timing of melt, in particular, are directly impacted by the changes in land surface albedo. This study presents an evaluation of the impact of assimilating Moderate Resolution Imaging Spectroradiometer (MODIS)-based surface albedo estimates in the Noah multi-parameterization (Noah-MP) land surface model, over the continental US during the time period from 2000 to 2017. The evaluation of simulated snow depth and snow cover fields show that significant improvements from data assimilation (DA) are obtained over the High Plains and parts of the Rocky Mountains. Earlier snowmelt and reduced agreements with reference snow depth measurements, primarily over the Northeast US, are also observed due to albedo DA. Most improvements from assimilation are observed over locations with moderate vegetation and lower elevation. The aggregate impact on evapotranspiration and runoff from assimilation is found to be marginal. This study also evaluates the relative and joint utility of assimilating fractional snow cover and surface albedo measurements. Relative to surface albedo assimilation, fractional snow cover assimilation is found to provide smaller improvements in the simulated snow depth fields. The configuration that jointly assimilates surface albedo and fractional snow cover measurements is found to provide the most beneficial improvements compared to the univariate DA configurations for surface albedo or fractional snow cover. Overall, the study also points to the need for improving the albedo formulations in land surface models and the incorporation of observational uncertainties within albedo DA configurations.

Snow accumulation and ablation in disturbed mountain spruce forest in West Tatra Mts

Biologia ◽  
2014 ◽  
Vol 69 (11) ◽  
Author(s):  
Martin Bartík ◽  
Roman Sitko ◽  
Marek Oreňák ◽  
Juraj Slovik ◽  
Jaroslav Škvarenina
Keyword(s):  
Snow Cover ◽  
Open Space ◽  
Snow Water Equivalent ◽  
Reference Value ◽  
Research Area ◽  
Snow Accumulation ◽  
Snow Density ◽  
Tatra Mts ◽  
Snow Water ◽  
The Impact

AbstractIn the presented paper we deal with the impact of the mature spruce stand on the accumulation and melting of snow cover at Červenec research area located in the Western Tatras at an elevation of 1420 m a.s.l. The work analyses the data obtained from the monitoring of snow cover during the period 2009–2014 (6 seasons). Since the season 2012/2013 the measurements have been also performed in a dead part of the stand and in a meadow. The results proved significant impact of the spruce stand on hydro-physical characteristics of snow cover — snow water equivalent, snow density, as well as on their change due to the dieback of the stand. The data measured at individual locations (open space in the forest, open meadow area, living and dead forest) were tested with the paired t-test for the significance of average differences. Average snow water equivalent in the living forest, dead forest and meadow was 42%, 47% and 83% of the reference value measured at the open space in the forest, respectively. The process of snow accumulation and melting was fastest at the open space, followed by the dead forest. In the living forest, the processes were the slowest.

The Impact of Snow Model Complexity at Three CLPX Sites

2008 ◽  
Vol 9 (6) ◽  
pp. 1464-1481 ◽  
Author(s):  
Xia Feng ◽  
Alok Sahoo ◽  
Kristi Arsenault ◽  
Paul Houser ◽  
Yan Luo ◽  
...  
Keyword(s):  
Snow Cover ◽  
Land Surface ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Snow Accumulation ◽  
Model Complexity ◽  
Snow Albedo ◽  
Community Land Model ◽  
Snow Model ◽  
The Impact

Abstract Many studies have developed snow process understanding by exploring the impact of snow model complexity on simulation performance. This paper revisits this topic using several recently developed land surface models, including the Simplified Simple Biosphere Model (SSiB); Noah; Variable Infiltration Capacity (VIC); Community Land Model, version 3 (CLM3); Snow Thermal Model (SNTHERM); and new field measurements from the Cold Land Processes Field Experiment (CLPX). Offline snow cover simulations using these five snow models with different physical complexity are performed for the Rabbit Ears Buffalo Pass (RB), Fraser Experimental Forest headquarters (FHQ), and Fraser Alpine (FA) sites between 20 September 2002 and 1 October 2003. These models simulate the snow accumulation and snowpack ablation with varying skill when forced with the same meteorological observations, initial conditions, and similar soil and vegetation parameters. All five models capture the basic features of snow cover dynamics but show remarkable discrepancy in depicting snow accumulation and ablation, which could result from uncertain model physics and/or biased forcing. The simulated snow depth in SSiB during the snow accumulation period is consistent with the more complicated CLM3 and SNTHERM; however, early runoff is noted, owing to neglected water retention within the snowpack. Noah is consistent with SSiB in simulating snow accumulation and ablation at RB and FA, but at FHQ, Noah underestimates snow depth and snow water equivalent (SWE) as a result of a higher net shortwave radiation at the surface, resulting from the use of a small predefined maximum snow albedo. VIC and SNTHERM are in good agreement with each other, and they realistically reproduce snow density and net radiation. CLM3 is consistent with VIC and SNTHERM during snow accumulation, but it shows early snow disappearance at FHQ and FA. It is also noted that VIC, CLM3, and SNTHERM are unable to capture the observed runoff timing, even though the water storage and refreezing effects are included in their physics. A set of sensitivity experiments suggest that Noah’s snow simulation is improved with a higher maximum albedo and that VIC exhibits little improvement with a larger fresh snow albedo. There are remarkable differences in the vegetation impact on snow simulation for each snow model. In the presence of forest cover, SSiB shows a substantial increase in snow depth and SWE, Noah and VIC show a slight change though VIC experiences a later onset of snowmelt, and CLM3 has a reduction in its snow depth. Finally, we observe that a refined precipitation dataset significantly improves snow simulation, emphasizing the importance of accurate meteorological forcing for land surface modeling.

scholarly journals Change in snow depletion pattern in a river basin of Arunachal Pradesh under projected climatic scenarios

2017 ◽  
Vol 19 (2) ◽  
pp. 199-210
Keyword(s):  
Snow Cover ◽  
Meteorological Data ◽  
Snow Accumulation ◽  
High Mountain ◽  
Present Climate ◽  
Arunachal Pradesh ◽  
Temperature And Precipitation ◽  
Depletion Curve ◽  
Climatic Scenarios ◽  
The Impact

<p>Snow depletion curves (SDCs) are important in hydrological studies for predicting snowmelt generated runoff in high mountain catchments. The present study deals with the derivation of the average snow depletion pattern in the Mago basin of Arunachal Pradesh, which falls in the eastern Himalayan region and the generation of climate affected SDCs in future years (2020, 2030, 2040, and 2050) under different projected climatic scenarios. The MODIS daily snow cover product at 500m resolution from both the Aqua and Terra satellites was used to obtain daily snow cover maps. MOD10A1 and MYD10A1 images were compared to select cloud free or minimum cloud image to obtain the temporal distribution of snow cover area (SCA). Snow accumulation and depletion patterns were obtained by analysing SCA at different days. For most of the years, two peaks were observed in the SCA analysis. The conventional depletion curve (CDC) representing present climate was derived by determining and interpolating the SCA from cloud-free (cloud&lt;5%) images for the selected hydrological year 2007. The investigation shows that the SCA was highest in February and lowest in May. Ten years meteorological data were used to normalize the temperature and precipitation data of the selected hydrological year (2007) to eliminate the impact of their yearly fluctuations on the snow cover depletion. The temperature and precipitation changes under four different projected climatic scenarios (A1B, A2, B1, and IPCC Commitment) were analysed for future years. Changes in the cumulative snowmelt depth with respect to the present climate for different future years were studied by a degree-day approach and were found to be highest under A1B, followed by A2, B1, and IPCC Commitment scenarios. It was observed that the A1B climatic scenario affected the depletion pattern most, making the depletion of snow to start and complete faster than under different scenarios. Advancing of depletion curve for different future years was found to be highest under A1B and lowest under IPCC Commitment scenarios with A2 and B1 in-between them.</p>

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