Projected sea-level changes in the marginal seas near China based on dynamical downscaling

2021 ◽  
pp. 1-52
Author(s):  
Yi Jin ◽  
Xuebin Zhang ◽  
John A. Church ◽  
Xianwen Bao
Keyword(s):  
Climate Change ◽  
Sea Level ◽  
Climate Models ◽  
Dynamical Downscaling ◽  
Global Climate Models ◽  
Open Boundary ◽  
Open Boundary Conditions ◽  
Sea Level Changes ◽  
Marginal Seas ◽  
The Pacific

AbstractProjections of future sea-level changes are usually based on global climate models (GCMs). However, the changes in shallow coastal regions, like the marginal seas near China, cannot be fully resolved in GCMs. To improve regional sea-level simulations, a high-resolution (~8 km) regional ocean model is set up for the marginal seas near China for both the historical (1994-2015) and future (2079-2100) periods under representative concentration pathways (RCPs) 4.5 and 8.5. The historical ocean simulations are evaluated at different spatiotemporal scales, and the model is then integrated for the future period, driven by projected monthly climatological climate change signals from 8 GCMs individually via both surface and open boundary conditions. The downscaled ocean changes derived by comparing historical and future experiments reveal greater spatial details than those from GCMs, e.g., a low dynamic sea level (DSL) centre of -0.15 m in the middle of the South China Sea (SCS). As a novel test, the downscaled results driven by the ensemble mean forcings are almost identical with the ensemble average results from individually downscaled cases. Forcing of the DSL change and increased cyclonic circulation in the SCS are dominated by the climate change signals from the Pacific, while the DSL change in the East China marginal seas is caused by both local atmosphere forcing and signals from the Pacific. The method of downscaling developed in this study is a useful modelling protocol for adaptation and mitigation planning for future oceanic climate changes.

scholarly journals Uncertainty Impacts of Climate Change and Downscaling Methods on Future Runoff Projections in the Biliu River Basin

Water ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 2130 ◽  
Author(s):  
Zhu ◽  
Zhang ◽  
Wu ◽  
Qi ◽  
Fu ◽  
...  
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Water Resource Management ◽  
Climate Models ◽  
Dynamical Downscaling ◽  
Global Climate ◽  
Global Climate Models ◽  
Liaoning Province ◽  
Lower Envelope ◽  
The Impact

This paper assesses the uncertainties in the projected future runoff resulting from climate change and downscaling methods in the Biliu River basin (Liaoning province, Northeast China). One widely used hydrological model SWAT, 11 Global Climate Models (GCMs), two statistical downscaling methods, four dynamical downscaling datasets, and two Representative Concentration Pathways (RCP4.5 and RCP8.5) are applied to construct 22 scenarios to project runoff. Hydrology variables in historical and future periods are compared to investigate their variations, and the uncertainties associated with climate change and downscaling methods are also analyzed. The results show that future temperatures will increase under all scenarios and will increase more under RCP8.5 than RCP4.5, while future precipitation will increase under 16 scenarios. Future runoff tends to decrease under 13 out of the 22 scenarios. We also found that the mean runoff changes ranging from −38.38% to 33.98%. Future monthly runoff increases in May, June, September, and October and decreases in all the other months. Different downscaling methods have little impact on the lower envelope of runoff, and they mainly impact the upper envelope of the runoff. The impact of climate change can be regarded as the main source of the runoff uncertainty during the flood period (from May to September), while the impact of downscaling methods can be regarded as the main source during the non-flood season (from October to April). This study separated the uncertainty impact of different factors, and the results could provide very important information for water resource management.

scholarly journals Local Sea-Level Rise Caused by Climate Change in the Northwest Pacific Marginal Seas Using Dynamical Downscaling

2021 ◽  
Vol 8 ◽  
Author(s):  
Yong-Yub Kim ◽  
Bong-Gwan Kim ◽  
Kwang Young Jeong ◽  
Eunil Lee ◽  
Do-Seong Byun ◽  
...  
Keyword(s):  
High Resolution ◽  
Sea Level Rise ◽  
Sea Level ◽  
Climate Models ◽  
Dynamical Downscaling ◽  
The Yellow Sea ◽  
Northwest Pacific ◽  
Tidal Amplitude ◽  
Marginal Seas ◽  
Rcp 8.5

Global climate models (GCMs) have limited capacity in simulating spatially non-uniform sea-level rise owing to their coarse resolutions and absence of tides in the marginal seas. Here, regional ocean climate models (RCMs) that consider tides were used to address these limitations in the Northwest Pacific marginal seas through dynamical downscaling. Four GCMs that drive the RCMs were selected based on a performance evaluation along the RCM boundaries, and the latter were validated by comparing historical results with observations. High-resolution (1/20°) RCMs were used to project non-uniform changes in the sea-level under intermediate (RCP 4.5) and high-end emissions (RCP 8.5) scenarios from 2006 to 2100. The predicted local sea-level rise was higher in the East/Japan Sea (EJS), where the currents and eddy motions were active. The tidal amplitude changes in response to sea-level rise were significant in the shallow areas of the Yellow Sea (YS). Dynamically downscaled simulations enabled the determination of practical sea-level rise (PSLR), including changes in tidal amplitude and natural variability. Under RCP 8.5 scenario, the maximum PSLR was ∼85 cm in the YS and East China Sea (ECS), and ∼78 cm in the EJS. The contribution of natural sea-level variability changes in the EJS was greater than that in the YS and ECS, whereas changes in the tidal contribution were higher in the YS and ECS. Accordingly, high-resolution RCMs provided spatially different PSLR estimates, indicating the importance of improving model resolution for local sea-level projections in marginal seas.

scholarly journals Utilization of Climate Files Predicting Future Weather in Dynamic Building Performance Simulation – A review

2021 ◽  
Vol 2069 (1) ◽  
pp. 012070
Author(s):  
C N Nielsen ◽  
J Kolarik
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Dynamical Downscaling ◽  
Global Climate ◽  
Global Climate Models ◽  
Weather Data ◽  
Building Performance ◽  
Comprehensive Overview ◽  
Local Climate ◽  
The Impact

Abstract As the climate is changing and buildings are designed with a life expectancy of 50+ years, it is sensible to take climate change into account during the design phase. Data representing future weather are needed so that building performance simulations can predict the impact of climate change. Currently, this usually requires one year of weather data with a temporal resolution of one hour, which represents local climate conditions. However, both the temporal and spatial resolution of global climate models is generally too coarse. Two general approaches to increase the resolution of climate models - statistical and dynamical downscaling have been developed. They exist in many variants and modifications. The present paper aims to provide a comprehensive overview of future weather application as well as critical insights in the model and method selection. The results indicate a general trend to select the simplest methods, which often involves a compromise on selecting climate models.

Subsampling Impact on the Climate Change Signal over Poland Based on Simulations from Statistical and Dynamical Downscaling

2019 ◽  
Vol 58 (5) ◽  
pp. 1061-1078 ◽  
Author(s):  
Abdelkader Mezghani ◽  
Andreas Dobler ◽  
Rasmus Benestad ◽  
Jan Erik Haugen ◽  
Kajsa M. Parding ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Climate Model ◽  
Emission Scenario ◽  
Dynamical Downscaling ◽  
Full Range ◽  
Global Climate Models ◽  
Climate Change Signal ◽  
High Emission ◽  
Selection Of

ABSTRACTMost impact studies using downscaled climate data as input assume that the selection of few global climate models (GCMs) representing the largest spread covers the likely range of future changes. This study shows that including more GCMs can result in a very different behavior. We tested the influence of selecting various subsets of GCMs on the climate change signal over Poland from simulations based on dynamical and empirical–statistical downscaling methods. When the climate variable is well simulated by the GCM, such as temperature, results showed that both downscaling methods agree on a warming over Poland by up to 2° or 5°C assuming intermediate or high emission scenarios, respectively, by 2071–2100. As a less robust simulated signal through GCMs, precipitation is expected to increase by up to 10% by 2071–2100 assuming the intermediate emission scenario. However, these changes are uncertain when the high emission scenario and the end of the twenty-first century are of interest. Further, an additional bootstrap test revealed an underestimation in the warming rate varying from 0.5° to more than 4°C over Poland that was found to be largely influenced by the selection of few driving GCMs instead of considering the full range of possible climate model outlooks. Furthermore, we found that differences between various combinations of small subsets from the GCM ensemble of opportunities can be as large as the climate change signal.

scholarly journals Impact of climate change on the stream flow of the lower Brahmaputra: trends in high and low flows based on discharge-weighted ensemble modelling

2011 ◽  
Vol 15 (5) ◽  
pp. 1537-1545 ◽  
Author(s):  
A. K. Gain ◽  
W. W. Immerzeel ◽  
F. C. Sperna Weiland ◽  
M. F. P. Bierkens
Keyword(s):  
Climate Change ◽  
Sea Level ◽  
Stream Flow ◽  
Climate Models ◽  
River Flow ◽  
Climate Model ◽  
Global Climate Models ◽  
Low Flow ◽  
Strong Increase ◽  
Hydrological Models

Abstract. Climate change is likely to have significant effects on the hydrology. The Ganges-Brahmaputra river basin is one of the most vulnerable areas in the world as it is subject to the combined effects of glacier melt, extreme monsoon rainfall and sea level rise. To what extent climate change will impact river flow in the Brahmaputra basin is yet unclear, as climate model studies show ambiguous results. In this study we investigate the effect of climate change on both low and high flows of the lower Brahmaputra. We apply a novel method of discharge-weighted ensemble modeling using model outputs from a global hydrological models forced with 12 different global climate models (GCMs). Our analysis shows that only a limited number of GCMs are required to reconstruct observed discharge. Based on the GCM outputs and long-term records of observed flow at Bahadurabad station, our method results in a multi-model weighted ensemble of transient stream flow for the period 1961–2100. Using the constructed transients, we subsequently project future trends in low and high river flow. The analysis shows that extreme low flow conditions are likely to occur less frequent in the future. However a very strong increase in peak flows is projected, which may, in combination with projected sea level change, have devastating effects for Bangladesh. The methods presented in this study are more widely applicable, in that existing multi-model streamflow simulations from global hydrological models can be weighted against observed streamflow data to assess at first order the effects of climate change for specific river basins.

Empirical-Statistical Downscaling: Nonlinear Statistical Downscaling

2018 ◽  
Author(s):  
Aristita Busuioc ◽  
Alexandru Dumitrescu
Keyword(s):  
Climate Change ◽  
Statistical Downscaling ◽  
Large Scale ◽  
Climate Models ◽  
Dynamical Downscaling ◽  
Global Climate ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Global Climate Models ◽  
Local Climate

This is an advance summary of a forthcoming article in the Oxford Research Encyclopedia of Climate Science. Please check back later for the full article.The concept of statistical downscaling or empirical-statistical downscaling became a distinct and important scientific approach in climate science in recent decades, when the climate change issue and assessment of climate change impact on various social and natural systems have become international challenges. Global climate models are the best tools for estimating future climate conditions. Even if improvements can be made in state-of-the art global climate models, in terms of spatial resolution and their performance in simulation of climate characteristics, they are still skillful only in reproducing large-scale feature of climate variability, such as global mean temperature or various circulation patterns (e.g., the North Atlantic Oscillation). However, these models are not able to provide reliable information on local climate characteristics (mean temperature, total precipitation), especially on extreme weather and climate events. The main reason for this failure is the influence of local geographical features on the local climate, as well as other factors related to surrounding large-scale conditions, the influence of which cannot be correctly taken into consideration by the current dynamical global models.Impact models, such as hydrological and crop models, need high resolution information on various climate parameters on the scale of a river basin or a farm, scales that are not available from the usual global climate models. Downscaling techniques produce regional climate information on finer scale, from global climate change scenarios, based on the assumption that there is a systematic link between the large-scale and local climate. Two types of downscaling approaches are known: a) dynamical downscaling is based on regional climate models nested in a global climate model; and b) statistical downscaling is based on developing statistical relationships between large-scale atmospheric variables (predictors), available from global climate models, and observed local-scale variables of interest (predictands).Various types of empirical-statistical downscaling approaches can be placed approximately in linear and nonlinear groupings. The empirical-statistical downscaling techniques focus more on details related to the nonlinear models—their validation, strengths, and weaknesses—in comparison to linear models or the mixed models combining the linear and nonlinear approaches. Stochastic models can be applied to daily and sub-daily precipitation in Romania, with a comparison to dynamical downscaling. Conditional stochastic models are generally specific for daily or sub-daily precipitation as predictand.A complex validation of the nonlinear statistical downscaling models, selection of the large-scale predictors, model ability to reproduce historical trends, extreme events, and the uncertainty related to future downscaled changes are important issues. A better estimation of the uncertainty related to downscaled climate change projections can be achieved by using ensembles of more global climate models as drivers, including their ability to simulate the input in downscaling models. Comparison between future statistical downscaled climate signals and those derived from dynamical downscaling driven by the same global model, including a complex validation of the regional climate models, gives a measure of the reliability of downscaled regional climate changes.

scholarly journals Probabilistic projection of sea-level change along the world's coastlines

2012 ◽  
Vol 3 (1) ◽  
pp. 357-389 ◽  
Author(s):  
M. Perrette ◽  
F. Landerer ◽  
R. Riva ◽  
K. Frieler ◽  
M. Meinshausen
Keyword(s):  
Climate Change ◽  
Sea Level ◽  
Impact Analysis ◽  
Climate Models ◽  
Ocean Dynamics ◽  
Sea Level Changes ◽  
Adaptation To Climate Change ◽  
Ocean Heat Uptake ◽  
The North ◽  
Global Mean

Abstract. Climate change causes global mean sea level to rise due to thermal expansion of seawater and loss of land ice from mountain glaciers, ice caps and ice-sheets. Locally, sea-level changes can strongly deviate from the global mean due to ocean dynamics. In addition, gravitational adjustments redistribute seawater away from shrinking ice masses, an effect currently not incorporated in climate models. Here, we provide probabilistic projections of sea level changes along the world's coastlines for the end of the 21st century under the new RCP emission scenarios, taking into account uncertainties across the cause-effect chain from greenhouse-gas emissions to ocean heat uptake and regional land-ice melt. At low latitudes, especially in the Indian Ocean and Western Pacific, sea level will likely rise more than the global mean (mostly by 10–20%, but up to 45% in Tokyo area). Around the North Atlantic and the North-Eastern Pacific coasts, sea level will rise less than the global average or, in some rare cases, even fall. Our probabilistic regional sea level projections provide an improved basis for coastal impact analysis and infrastructure planning for adaptation to climate change.

scholarly journals Modeling of Future Sea Level Rise Through Melting Glaciers

2015 ◽  
Vol 5 (1) ◽  
Author(s):  
Manoj Kumar Singh ◽  
Bharat Raj Singh
Keyword(s):  
Sea Level Rise ◽  
Sea Level ◽  
21St Century ◽  
Climate Models ◽  
Global Climate ◽  
Global Climate Models ◽  
Sea Level Changes ◽  
Systematic Analysis ◽  
Ice Caps ◽  
Mountain Glaciers

The aim of this paper is to project 21st century volume changes of all mountain glacier and ice caps and to provide systematic analysis of uncertainties originating from different sources in the and their contribution to sea level rise and the assessment of uncertainties. Trends in global climate warming and sea level rise are observed during the last 100-years which both, according to global climate models, will continue in the future Intergovernmental Panel on Climate Change (IPCC) State-of-threat knowledge on climate, ocean and land processes identifies melting mountain glaciers and ice caps, after ocean thermal expansion, as the currently second major contributor to sea level rise. However, both the observations and models on sea level changes carry a variety of uncertainties. In this section, by following the question-answer concept, I will briefly present the importance of global sea level change for society, the current state of knowledge of sea level changes in response to climate change and the attempts to project future sea level changes until 2100 including discussion on related uncertainties. Melting mountain glaciers and ice caps (MG&IC) are the second largest contributor to rising sea level after thermal expansion of the oceans and are likely to remain the dominant glaciological contributor to rising sea level in the 21st century. The aim of this work is to project 21st century volume changes of all MG&IC and to provide systematic analysis of uncertainties originating from different sources in the calculation. I provide an ensemble of 21st century volume rojections for all MG&IC from the World Glacier Inventory by modeling the surface mass balance coupled with volume-area-length scaling and forced with temperature and precipitation scenarios from four Global Climate Models (GCMs). By upscaling the volume projections through a regionally differentiated approach to all MG&IC outside Greenland and Antarctica (514,380 km2) I stimated total volume loss for the time period 2001-2100 to range from 0.039 to 0.150 m sea level equivalent. While three GCMs agree that Alaskan glaciers are the main contributors to the projected sea level rise, one GCM projected the largest total volume loss mainly due to Arctic MG&IC.

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