scholarly journals A Bayesian stochastic generator to complement existing climate change scenarios: supporting uncertainty quantification in marine and coastal ecosystems

2020 ◽  
Author(s):  
Lőrinc Mészáros ◽  
Frank van der Meulen ◽  
Geurt Jongbloed ◽  
Ghada El Serafy
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Coastal Ecosystems ◽  
Time Series Model ◽  
Ecological Impacts ◽  
Time Shift ◽  
Model Parameters ◽  
Climate Change Scenarios ◽  
Climate Variables ◽  
Residual Term

AbstractAvailable climate change projections, which can be used for quantifying future changes in marine and coastal ecosystems, usually consist of a few scenarios. Studies addressing ecological impacts of climate change often make use of a low- (RCP2.6), moderate- (RCP4.5) or high climate scenario (RCP8.5), without taking into account further uncertainties in these scenarios. In this research a methodology is proposed to generate further synthetic scenarios, based on existing datasets, for a better representation of climate change induced uncertainties. The methodology builds on Regional Climate Model scenarios provided by the EURO-CORDEX experiment. In order to generate new realizations of climate variables, such as radiation or temperature, a hierarchical Bayesian model is developed. In addition, a parameterized time series model is introduced, which includes a linear trend component, a seasonal shape with varying amplitude and time shift, and an additive residual term. The seasonal shape is derived with the non-parametric locally weighted scatterplot smoothing, and the residual term includes the smoothed variance of residuals and independent and identically distributed noise. The distributions of the time series model parameters are estimated through Bayesian parameter inference with Markov chain Monte Carlo sampling (Gibbs sampler). By sampling from the predictive distribution numerous new statistically representative synthetic scenarios can be generated including uncertainty estimates. As a demonstration case, utilizing these generated synthetic scenarios and a physically based ecological model (Delft3D-WAQ) that relates climate variables to ecosystem variables, a probabilistic simulation is conducted to further propagate the climate change induced uncertainties to marine and coastal ecosystem indicators.

scholarly journals Future Runoff Analysis in the Mekong River Basin under a Climate Change Scenario Using Deep Learning

Water ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1556 ◽  
Author(s):  
Daeeop Lee ◽  
Giha Lee ◽  
Seongwon Kim ◽  
Sungho Jung
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Climate Change Scenario ◽  
Swat Model ◽  
Mekong River ◽  
Change Scenario ◽  
Climate Change Scenarios ◽  
Rainfall Runoff ◽  
Runoff Prediction ◽  
Runoff Analysis

In establishing adequate climate change policies regarding water resource development and management, the most essential step is performing a rainfall-runoff analysis. To this end, although several physical models have been developed and tested in many studies, they require a complex grid-based parameterization that uses climate, topography, land-use, and geology data to simulate spatiotemporal runoff. Furthermore, physical rainfall-runoff models also suffer from uncertainty originating from insufficient data quality and quantity, unreliable parameters, and imperfect model structures. As an alternative, this study proposes a rainfall-runoff analysis system for the Kratie station on the Mekong River mainstream using the long short-term memory (LSTM) model, a data-based black-box method. Future runoff variations were simulated by applying a climate change scenario. To assess the applicability of the LSTM model, its result was compared with a runoff analysis using the Soil and Water Assessment Tool (SWAT) model. The following steps (dataset periods in parentheses) were carried out within the SWAT approach: parameter correction (2000–2005), verification (2006–2007), and prediction (2008–2100), while the LSTM model went through the process of training (1980–2005), verification (2006–2007), and prediction (2008–2100). Globally available data were fed into the algorithms, with the exception of the observed discharge and temperature data, which could not be acquired. The bias-corrected Representative Concentration Pathways (RCPs) 4.5 and 8.5 climate change scenarios were used to predict future runoff. When the reproducibility at the Kratie station for the verification period of the two models (2006–2007) was evaluated, the SWAT model showed a Nash–Sutcliffe efficiency (NSE) value of 0.84, while the LSTM model showed a higher accuracy, NSE = 0.99. The trend analysis result of the runoff prediction for the Kratie station over the 2008–2100 period did not show a statistically significant trend for neither scenario nor model. However, both models found that the annual mean flow rate in the RCP 8.5 scenario showed greater variability than in the RCP 4.5 scenario. These findings confirm that the LSTM runoff prediction presents a higher reproducibility than that of the SWAT model in simulating runoff variation according to time-series changes. Therefore, the LSTM model, which derives relatively accurate results with a small amount of data, is an effective approach to large-scale hydrologic modeling when only runoff time-series are available.

scholarly journals Grassland production under global change scenarios for New Zealand pastoral agriculture

2014 ◽  
Vol 7 (5) ◽  
pp. 2359-2391 ◽  
Author(s):  
E. D. Keller ◽  
W. T. Baisden ◽  
L. Timar ◽  
B. Mullan ◽  
A. Clark
Keyword(s):  
Climate Change ◽  
Land Use ◽  
New Zealand ◽  
Land Use Change ◽  
Model Parameters ◽  
Agricultural Activity ◽  
High Fertility ◽  
Climate Change Scenarios ◽  
Pasture Production ◽  
N Cycle

Abstract. We adapt and integrate the Biome-BGC and Land Use in Rural New Zealand models to simulate pastoral agriculture and to make land-use change, intensification of agricultural activity and climate change scenario projections of New Zealand's pasture production at time slices centred on 2020, 2050 and 2100, with comparison to a present-day baseline. Biome-BGC model parameters are optimised for pasture production in both dairy and sheep/beef farm systems, representing a new application of the Biome-BGC model. Results show up to a 10% increase in New Zealand's national pasture production in 2020 under intensification and a 1–2% increase by 2050 from economic factors driving land-use change. Climate change scenarios using statistically downscaled global climate models (GCMs) from the IPCC Fourth Assessment Report also show national increases of 1–2% in 2050, with significant regional variations. Projected out to 2100, however, these scenarios are more sensitive to the type of pasture system and the severity of warming: dairy systems show an increase in production of 4% under mild change but a decline of 1% under a more extreme case, whereas sheep/beef production declines in both cases by 3 and 13%, respectively. Our results suggest that high-fertility systems such as dairying could be more resilient under future change, with dairy production increasing or only slightly declining in all of our scenarios. These are the first national-scale estimates using a model to evaluate the joint effects of climate change, CO2 fertilisation and N-cycle feedbacks on New Zealand's unique pastoral production systems that dominate the nation's agriculture and economy. Model results emphasise that CO2 fertilisation and N-cycle feedback effects are responsible for meaningful differences in agricultural systems. More broadly, we demonstrate that our model output enables analysis of decoupled land-use change scenarios: the Biome-BGC data products at a national or regional level can be re-sampled quickly and cost-effectively for specific land-use change scenarios and future projections.

scholarly journals Climate Forcings and Climate Sensitivities Diagnosed from Coupled Climate Model Integrations

2006 ◽  
Vol 19 (23) ◽  
pp. 6181-6194 ◽  
Author(s):  
Piers Mde F. Forster ◽  
Karl E. Taylor
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Surface Temperature ◽  
Radiative Forcing ◽  
Climate Forcing ◽  
Lapse Rate ◽  
Climate Feedback ◽  
Short Time Scale ◽  
Climate Change Scenarios ◽  
Climate Forcings

Abstract A simple technique is proposed for calculating global mean climate forcing from transient integrations of coupled atmosphere–ocean general circulation models (AOGCMs). This “climate forcing” differs from the conventionally defined radiative forcing as it includes semidirect effects that account for certain short time scale responses in the troposphere. First, a climate feedback term is calculated from reported values of 2 × CO2 radiative forcing and surface temperature time series from 70-yr simulations by 20 AOGCMs. In these simulations carbon dioxide is increased by 1% yr−1. The derived climate feedback agrees well with values that are diagnosed from equilibrium climate change experiments of slab-ocean versions of the same models. These climate feedback terms are associated with the fast, quasi-linear response of lapse rate, clouds, water vapor, and albedo to global surface temperature changes. The importance of the feedbacks is gauged by their impact on the radiative fluxes at the top of the atmosphere. Partial compensation is found between longwave and shortwave feedback terms that lessens the intermodel differences in the equilibrium climate sensitivity. There is also some indication that the AOGCMs overestimate the strength of the positive longwave feedback. These feedback terms are then used to infer the shortwave and longwave time series of climate forcing in twentieth- and twenty-first-century simulations in the AOGCMs. The technique is validated using conventionally calculated forcing time series from four AOGCMs. In these AOGCMs the shortwave and longwave climate forcings that are diagnosed agree with the conventional forcing time series within ∼10%. The shortwave forcing time series exhibit order of magnitude variations between the AOGCMs, differences likely related to how both natural forcings and/or anthropogenic aerosol effects are included. There are also factor of 2 differences in the longwave climate forcing time series, which may indicate problems with the modeling of well-mixed greenhouse gas changes. The simple diagnoses presented provides an important and useful first step for understanding differences in AOGCM integrations, indicating that some of the differences in model projections can be attributed to different prescribed climate forcing, even for so-called standard climate change scenarios.

scholarly journals Quantifying excess deaths related to heatwaves under climate change scenarios: A multicountry time series modelling study

PLoS Medicine ◽  
2018 ◽  
Vol 15 (7) ◽  
pp. e1002629 ◽  
Author(s):  
Yuming Guo ◽  
Antonio Gasparrini ◽  
Shanshan Li ◽  
Francesco Sera ◽  
Ana Maria Vicedo-Cabrera ◽  
...  
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Climate Change Scenarios ◽  
Time Series Modelling ◽  
Excess Deaths ◽  
Modelling Study

scholarly journals Methods for the Design of Climate Change Scenario in Slovakia for the 21St Century

2009 ◽  
Vol 1 (1) ◽  
pp. 77-90 ◽  
Author(s):  
Marian Melo ◽  
Milan Lapin ◽  
Ingrid Damborska
Keyword(s):  
Climate Change ◽  
Time Series ◽  
21St Century ◽  
Climate Change Scenario ◽  
Change Scenario ◽  
Climate Change Scenarios ◽  
Precipitation Time Series ◽  
Impact Studies ◽  
Temperature And Precipitation ◽  
Mean Temperature

Abstract In this paper methods of climate-change scenario projection in Slovakia for the 21st century are outlined. Temperature and precipitation time series of the Hurbanovo Observatory in 1871-2007 (Slovak Hydrometeorological Institute) and data from four global GCMs (GISS 1998, CGCM1, CGCM2, HadCM3) are utilized for the design of climate change scenarios. Selected results of different climate change scenarios (based on different methods) for the region of Slovakia (up to 2100) are presented. The increase in annual mean temperature is about 3°C, though the results are ambiguous in the case of precipitation. These scenarios are required by users in impact studies, mainly from the hydrology, agriculture and forestry sectors.

scholarly journals Grassland production under global change scenarios for New Zealand pastoral agriculture

2014 ◽  
Vol 7 (3) ◽  
pp. 3307-3365
Author(s):  
E. D. Keller ◽  
W. T. Baisden ◽  
L. Timar ◽  
B. Mullan ◽  
A. Clark
Keyword(s):  
Climate Change ◽  
Land Use ◽  
New Zealand ◽  
Land Use Change ◽  
Global Climate Models ◽  
Model Parameters ◽  
High Fertility ◽  
Climate Change Scenarios ◽  
Pasture Production ◽  
N Cycle

Abstract. We adapt and integrate the Biome-BGC and Land Use in Rural New Zealand (LURNZ) models to simulate pastoral agriculture and to make land-use change, intensification and climate change scenario projections of New Zealand's pasture production at time slices centred on 2020, 2050 and 2100, with comparison to a present-day baseline. Biome-BGC model parameters are optimised for pasture production in both dairy and sheep/beef farm systems, representing a new application of the Biome-BGC model. Results show up to a 10% increase in New Zealand's national pasture production in 2020 under intensification and a 1–2% increase by 2050 from economic factors driving land-use change. Climate change scenarios using statistically downscaled global climate models (GCMs) from the IPCC Fourth Assessment Report (AR4) also show national increases of 1–2% in 2050, with significant regional variations. Projected out to 2100, however, these scenarios are more sensitive to the type of pasture system and the severity of warming: dairy systems show an increase in production of 4% under mild change but a decline of 1% under a more extreme case, whereas sheep/beef production declines in both cases by 3% and 13%, respectively. Our results suggest that high-fertility systems such as dairying could be more resilient under future change, with dairy production increasing or only slightly declining in all of our scenarios. These are the first national-scale estimates using a model to evaluate the joint effects of climate change, CO2 fertilisation and N-cycle feedbacks on New Zealand's unique pastoral production systems that dominate the nation's agriculture and economy. Model results emphasize that CO2 fertilisation and N cycle feedback effects are responsible for meaningful differences in agricultural systems. More broadly, we demonstrate that our model output enables analysis of Decoupled Land-Use Change Scenarios (DLUCS): the Biome-BGC data products at a national or regional level can be re-sampled quickly and cost-effectively for specific land-use change scenarios and future projections.

scholarly journals Identifying dynamical time series model parameters from equilibrium samples, with application to gene regulatory networks

2018 ◽  
Vol 19 (4) ◽  
pp. 444-465
Author(s):  
William Chad Young ◽  
Ka Yee Yeung ◽  
Adrian E Raftery
Keyword(s):  
Gene Expression ◽  
Time Series ◽  
Steady State ◽  
Gene Expression Data ◽  
Regulatory Networks ◽  
Network Inference ◽  
Time Series Model ◽  
Model Parameters ◽  
Expression Data ◽  
Gene Regulatory

Gene regulatory network reconstruction is an essential task of genomics in order to further our understanding of how genes interact dynamically with each other. The most readily available data, however, are from steady-state observations. These data are not as informative about the relational dynamics between genes as knockout or over-expression experiments, which attempt to control the expression of individual genes. We develop a new framework for network inference using samples from the equilibrium distribution of a vector autoregressive (VAR) time-series model which can be applied to steady-state gene expression data. We explore the theoretical aspects of our method and apply the method to synthetic gene expression data generated using GeneNetWeaver.

scholarly journals Structural Damage Assessment by Using Stiffness-based Method and Cubic Spline Interpolation

2018 ◽  
Vol 201 ◽  
pp. 01004
Author(s):  
Wei-Chih Su ◽  
Chiung-Shiann Huang ◽  
Jyh-Horng Wu
Keyword(s):  
Time Series ◽  
Stiffness Matrix ◽  
Spline Interpolation ◽  
Coefficient Matrix ◽  
Cubic Spline ◽  
Time Series Model ◽  
Recursive Least Squares ◽  
Model Parameters ◽  
Structural Dynamic ◽  
Cubic Spline Interpolation

This study explores the possibility of using stiffness-based method and cubic spline interpolation to locate the damaged storey of a building during a strong earthquake, and corresponding stiffness matrix of structure often change in the earthquake process. The time series model of a building is established from the full structural dynamic responses. Next, the coefficient matrix of the time series model could be solved by recursive least squares (RLS) algorithms. Then, the model parameters of a building are calculated by the coefficient matrix of time series model. Finally, the identified natural frequencies and mode shapes of structure that corrected by cubic spline interpolation would be used to construct the stiffness matrix of a building. Then, the damage location of a building could be detected by the identified stiffness matrix of a building. The effectiveness of the proposed procedure is verified using numerically simulated earthquake responses of the finite element model of a six-storey frame.

scholarly journals Application of LARS - WG downscaling model for building climate change scenarios in the Srepok watershed

2014 ◽  
Vol 17 (2) ◽  
pp. 108-122
Author(s):  
Khoi Nguyen Dao ◽  
Nhung Thi Hong Nguyen ◽  
Canh Thanh Truong
Keyword(s):  
Climate Change ◽  
General Circulation ◽  
Emission Scenario ◽  
Circulation Model ◽  
Climate Change Scenarios ◽  
Climate Variables ◽  
Climate Data ◽  
Policy Makers ◽  
Rain Gauges ◽  
Temperature And Precipitation

There are statistical downscaling methods such as: SDSM, LARS-WG, WGEN…, used to convert information on climate variables from the simulation results of General Circulation Model (GCM) to build climate change scenarios for local region. In this study, we used the LARS-WG model and HadCM3 GCM for two emission scenarios: B1 (low emission scenario) and A1B (medium emission scenario) to generate future scenarios for temperature and precipitation at meteorological stations and rain gauges in the Srepok watershed. The LARS-WG model was calibrated and validated against observed climate data for the period 1980-2009, and the calibrated LARS-WG was then used to generate future climate variables for the 2020s (2011-2030), 2055s (2046-2065), and 2090s (2080-2099). The climate change scenarios suggested that the climate in the study area will become warmer and drier in the future. The results obtained in this study could be useful for policy makers in planning climate change adaptation strategies for the study area.

Sign in / Sign up

Export Citation Format

Share Document