scholarly journals Projections of Future Climate Change in Singapore Based on a Multi-Site Multivariate Downscaling Approach

Water ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 2300 ◽  
Author(s):  
Xin Li ◽  
Ke Zhang ◽  
Vladan Babovic
Keyword(s):  
Climate Change ◽  
Global Climate ◽  
Global Climate Model ◽  
Temporal Structure ◽  
Global Climate Models ◽  
Future Climate Change ◽  
Daily Maximum ◽  
Monthly Precipitation ◽  
Projected Change ◽  
Projected Changes

Estimates of the projected changes in precipitation and temperature have great significance for adaption planning in the context of climate change. To obtain the climate change information at regional or local scale, downscaling approaches are required to downscale the coarse global climate model (GCM) outputs to finer resolutions in both spatial and temporal dimensions. The multi-site, multi-variate downscaling approach has received considerable attention recently due to its advantage in providing distributed, physically coherent downscaled meteorological fields for subsequent impact modeling. In this study, a newly developed multi-site multivariate statistical downscaling approach based on empirical copula was applied to downscale grid-based, monthly precipitation, maximum and minimum temperature outputs from nine global climate models to site-specific, daily data over four weather stations in Singapore. The advantage of this approach lies in its ability to reflect the at-site statistics, inter-site and inter-variable dependencies, and temporal structure in the downscaled data. The downscaling was conducted for two projection periods (i.e., the 2021–2050 and 2071–2100 periods) under two emission scenarios (i.e., representative concentration pathway (RCP)4.5 and RCP8.5 scenarios). Based on the downscaling results, projected changes in daily precipitation, maximum and minimum temperatures were examined. The results show that there is no consensus on the projected change in average precipitation over the two future periods. The major uncertainty for precipitation projection comes from the GCMs. For daily maximum and minimum temperatures, all downscaled GCMs project an increase of average temperature in the future. These change signals could be different from those of the original GCM data, both in magnitude and in direction. These findings could assist in adaption planning in Singapore in response to emerging climate risks.

scholarly journals Aspects of climate change prediction relevant to crop productivity

2005 ◽  
Vol 360 (1463) ◽  
pp. 1999-2009 ◽  
Author(s):  
Chris Huntingford ◽  
F Hugo Lambert ◽  
John H.C Gash ◽  
Christopher M Taylor ◽  
Andrew J Challinor
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Global Climate ◽  
Crop Productivity ◽  
Global Climate Models ◽  
Ease Of Use ◽  
Crop Models ◽  
Vulnerability To Climate Change ◽  
Projected Change ◽  
Projected Changes

Projected changes in surface climate are reviewed at a range of temporal scales, with an emphasis on tropical northern Africa—a region considered to be particularly vulnerable to climate change. Noting the key aspects of ‘weather’ affecting crop yield, we then consider relevant and projected change using output from a range of state of the art global climate models (GCMs), and for different future emission scenarios. The outputs from the models reveal significant inter-model variation in the change expected by the end of the twenty-first century for even the lowest IPCC emission scenario. We provide a set of recommendations on future model diagnostics, configurations and ease of use to close further the gap between GCMs and smaller-scale crop models. This has the potential to empower countries to make their own assessments of vulnerability to climate change induced periods of food scarcity.

scholarly journals Robust increase of Indian monsoon rainfall and its variability under future warming in CMIP-6 models

2020 ◽  
Author(s):  
Anja Katzenberger ◽  
Jacob Schewe ◽  
Julia Pongratz ◽  
Anders Levermann
Keyword(s):  
Climate Change ◽  
Global Warming ◽  
Climate Models ◽  
Monsoon Rainfall ◽  
Global Climate ◽  
Coupled Model ◽  
Global Climate Models ◽  
Future Climate Change ◽  
Global Mean Temperature ◽  
Almost All

Abstract. The Indian summer monsoon is an integral part of the global climate system. As its seasonal rainfall plays a crucial role in India's agriculture and shapes many other aspects of life, it affects the livelihood of a fifth of the world's population. It is therefore highly relevant to assess its change under potential future climate change. Global climate models within the Coupled Model Intercomparison Project Phase 5 (CMIP-5) indicated a consistent increase in monsoon rainfall and its variability under global warming. Since the range of the results of CMIP-5 was still large and the confidence in the models was limited due to partly poor representation of observed rainfall, the updates within the latest generation of climate models in CMIP-6 are of interest. Here, we analyse 32 models of the latest CMIP-6 exercise with regard to their annual mean monsoon rainfall and its variability. All of these models show a substantial increase in June-to-September (JJAS) mean rainfall under unabated climate change (SSP5-8.5) and most do also for the other three Shared Socioeconomic Pathways analyzed (SSP1-2.6, SSP2-4.5, SSP3-7.0). Moreover, the simulation ensemble indicates a linear dependence of rainfall on global mean temperature with high agreement between the models and independent of the SSP; the multi-model mean for JJAS projects an increase of 0.33 mm/day and 5.3 % per degree of global warming. This is significantly higher than in the CMIP-5 projections. Most models project that the increase will contribute to the precipitation especially in the Himalaya region and to the northeast of the Bay of Bengal, as well as the west coast of India. Interannual variability is found to be increasing in the higher-warming scenarios by almost all models. The CMIP-6 simulations largely confirm the findings from CMIP-5 models, but show an increased robustness across models with reduced uncertainties and updated magnitudes towards a stronger increase in monsoon rainfall.

scholarly journals Assessing the impact of global warming on worldwide open field tomato cultivation through CSIRO-Mk3·0 global climate model

2016 ◽  
Vol 155 (3) ◽  
pp. 407-420 ◽  
Author(s):  
R. S. SILVA ◽  
L. KUMAR ◽  
F. SHABANI ◽  
M. C. PICANÇO
Keyword(s):  
Climate Change ◽  
Global Warming ◽  
Open Field ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Current Model ◽  
Global Climate Models ◽  
The Impact

SUMMARYTomato (Solanum lycopersicum L.) is one of the most important vegetable crops globally and an important agricultural sector for generating employment. Open field cultivation of tomatoes exposes the crop to climatic conditions, whereas greenhouse production is protected. Hence, global warming will have a greater impact on open field cultivation of tomatoes rather than the controlled greenhouse environment. Although the scale of potential impacts is uncertain, there are techniques that can be implemented to predict these impacts. Global climate models (GCMs) are useful tools for the analysis of possible impacts on a species. The current study aims to determine the impacts of climate change and the major factors of abiotic stress that limit the open field cultivation of tomatoes in both the present and future, based on predicted global climate change using CLIMatic indEX and the A2 emissions scenario, together with the GCM Commonwealth Scientific and Industrial Research Organisation (CSIRO)-Mk3·0 (CS), for the years 2050 and 2100. The results indicate that large areas that currently have an optimum climate will become climatically marginal or unsuitable for open field cultivation of tomatoes due to progressively increasing heat and dry stress in the future. Conversely, large areas now marginal and unsuitable for open field cultivation of tomatoes will become suitable or optimal due to a decrease in cold stress. The current model may be useful for plant geneticists and horticulturalists who could develop new regional stress-resilient tomato cultivars based on needs related to these modelling projections.

The Impact of Climate Change on the Long-Term Response of Offshore Structures: A Study Case

2019 ◽  
Author(s):  
Irvin Alberto Mosquera ◽  
Luis Volnei Sudati Sagrilo ◽  
Paulo Maurício Videiro
Keyword(s):  
Climate Change ◽  
Global Climate ◽  
Global Climate Model ◽  
Offshore Structures ◽  
Global Climate Models ◽  
Wave Parameter ◽  
Greenhouse Emissions ◽  
The Impact ◽  
Term Response

Abstract This paper discusses the influence of the climate change in the long-term response of offshore structures. The case studied is a linear single-degree-of-freedom (SDOF) system under environmental load wave characterized by the JONSWAP spectrum. The wave parameter data used in the analyses were obtained from running the wind wave WaveWatch III with wind field input data derived from two Global Climate Models (GCMs): HadGEM2-ES and MRI-CGCM3 considering historical and future greenhouse emissions scenarios. The study was carried out for two locations: one in the North Atlantic and the other in Brazilian South East Coast. Environmental contours have been used to estimate the extreme long-term response. The results suggest that climate change would affect the structure response and its impact is highly depend on the structure location, the global climate model and the greenhouse emissions scenario selected.

Evaluation of Climate Change Impact on Sustainability of Tailan Underground Reservoir in China

2013 ◽  
Vol 726-731 ◽  
pp. 3249-3255
Author(s):  
Emmanuel Kwame Appiah-Adjei ◽  
Long Cang Shu ◽  
Kwaku Amaning Adjei ◽  
Cheng Peng Lu
Keyword(s):  
Climate Change ◽  
Water Balance ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Future Climate ◽  
Future Climate Change ◽  
Research Station ◽  
Recharge Sources ◽  
Underground Reservoir

In order to ensure availability of water throughout the year in the Tailan River basin of northwestern China, an underground reservoir has been constructed in the basin to augment the groundwater resource and efficiently utilize it. This study investigates the potential impact of future climate change on the reservoir by assessing its influence on sustainability of recharge sources to the reservoir. The methods employed involved using a combined Statistical Downscaling Model (SDSM) and Long Ashton Research Station Weather Generator (LARS-WG) to downscale the climate variations of the basin from a global climate model and applying them through a simple soil water balance to quantify their impact on recharge to the reservoir. The results predict the current mean monthly temperature of the basin to increase by 2.01°C and 2.84°C for the future periods 2040-2069 and 2070-2099, respectively, while the precipitations are to decrease by 25% and 36% over the same periods. Consequently, the water balance analyses project the recharge to the reservoir to decrease by 37% and 49% for the periods 2040-2069 and 2070-2099, respectively. Thus the study provides useful information for sustainable management of the reservoir against potential future climate changes.

scholarly journals Impacts of future climate change on potential yields of major crops in China

2014 ◽  
Vol 5 (1) ◽  
pp. 617-647
Author(s):  
Y. Yin ◽  
Q. Tang ◽  
X. Liu
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Global Climate ◽  
Global Climate Models ◽  
Future Climate ◽  
Future Climate Change ◽  
Yield Reduction ◽  
Climate Data ◽  
Potential Yield ◽  
Crop Models

Abstract. Climate change may affect crop development and yield, and consequently cast a shadow of doubt over China's food self-sufficiency efforts. In this study we used the model projections of a couple of global gridded crop models (GGCMs) to assess the effects of future climate change on the potential yields of the major crops (i.e. wheat, rice, maize and soybean) over China. The GGCMs were forced with the bias-corrected climate data from 5 global climate models (GCMs) under the Representative Concentration Pathways (RCP) 8.5 which were made available by the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP). The results show that the potential yields of rice may increase over a large portion of China. Climate change may benefit food productions over the high-altitude and cold regions where are outside current main agricultural area. However, the potential yield of maize, soybean and wheat may decrease in a large portion of the current main crop planting areas such as North China Plain. Development of new agronomic management strategy may be useful for coping with climate change in the areas with high risk of yield reduction.

Downscaling Climate Information

2016 ◽  
Author(s):  
Rasmus Benestad
Keyword(s):  
Climate Change ◽  
Large Scale ◽  
Climate Models ◽  
Southern Oscillation ◽  
Global Climate ◽  
Global Climate Model ◽  
Global Climate Models ◽  
Local Climate ◽  
Skill Scores ◽  
The Right

What are the local consequences of a global climate change? This question is important for proper handling of risks associated with weather and climate. It also tacitly assumes that there is a systematic link between conditions taking place on a global scale and local effects. It is the utilization of the dependency of local climate on the global picture that is the backbone of downscaling; however, it is perhaps easiest to explain the concept of downscaling in climate research if we start asking why it is necessary. Global climate models are our best tools for computing future temperature, wind, and precipitation (or other climatological variables), but their limitations do not let them calculate local details for these quantities. It is simply not adequate to interpolate from model results. However, the models are able to predict large-scale features, such as circulation patterns, El Niño Southern Oscillation (ENSO), and the global mean temperature. The local temperature and precipitation are nevertheless related to conditions taking place over a larger surrounding region as well as local geographical features (also true, in general, for variables connected to weather/climate). This, of course, also applies to other weather elements. Downscaling makes use of systematic dependencies between local conditions and large-scale ambient phenomena in addition to including information about the effect of the local geography on the local climate. The application of downscaling can involve several different approaches. This article will discuss various downscaling strategies and methods and will elaborate on their rationale, assumptions, strengths, and weaknesses. One important issue is the presence of spontaneous natural year-to-year variations that are not necessarily directly related to the global state, but are internally generated and superimposed on the long-term climate change. These variations typically involve phenomena such as ENSO, the North Atlantic Oscillation (NAO), and the Southeast Asian monsoon, which are nonlinear and non-deterministic. We cannot predict the exact evolution of non-deterministic natural variations beyond a short time horizon. It is possible nevertheless to estimate probabilities for their future state based, for instance, on projections with models run many times with slightly different set-up, and thereby to get some information about the likelihood of future outcomes. When it comes to downscaling and predicting regional and local climate, it is important to use many global climate model predictions. Another important point is to apply proper validation to make sure the models give skillful predictions. For some downscaling approaches such as regional climate models, there usually is a need for bias adjustment due to model imperfections. This means the downscaling doesn’t get the right answer for the right reason. Some of the explanations for the presence of biases in the results may be different parameterization schemes in the driving global and the nested regional models. A final underlying question is: What can we learn from downscaling? The context for the analysis is important, as downscaling is often used to find answers to some (implicit) question and can be a means of extracting most of the relevant information concerning the local climate. It is also important to include discussions about uncertainty, model skill or shortcomings, model validation, and skill scores.

scholarly journals Effect of Model Resolution on Intense and Extreme Precipitationinthe Mediterranean Region

Atmosphere ◽  
2020 ◽  
Vol 11 (7) ◽  
pp. 699
Author(s):  
Dario Conte ◽  
Silvio Gualdi ◽  
Piero Lionello
Keyword(s):  
Climate Change ◽  
Mediterranean Region ◽  
Climate Models ◽  
Global Climate ◽  
Global Climate Model ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Global Climate Models ◽  
Total Precipitation ◽  
Model Resolution

This study explores the role of model resolution on the simulation of precipitation and on the estimate of its future change in the Mediterranean region. It compares the results of two regional climate models (RCMs, with two different horizontal grid resolutions, 0.44 and 0.11 degs, covering the whole Mediterranean region) and of the global climate model (GCM, 0.75 degs) that has provided the boundary conditions for them. The regional climate models include an interactive oceanic component with a resolution of 1/16 degs. The period 1960–2100 and the representative concentration pathways RCP4.5 and RCP8.5 are considered. The results show that, in the present climate, increasing resolution increases total precipitation and its extremes over steep orography, while it has the opposite effect over flat areas and the sea. Considering climate change, in all simulations, total precipitation will decrease over most of the considered domain except at the northern boundary, where it will increase. Extreme precipitation will increase over most of the northern Mediterranean region and decrease over the sea and some southern areas. Further, the overall probability of precipitation (frequency of wet days) significantly decreases over most of the region, but wet days will be characterized with precipitation intensity higher than the present. Our analysis shows that: (1) these projected changes are robust with respect to the considered range of model resolution; (2) increasing the resolution (within the considered resolution range) decreases the magnitude of these climate change effects. However, it is likely that resolution plays a less important role than other factors, such as the different physics of regional and global climate models. It remains to be investigated whether further increasing the resolution (and reaching the scale explicitly permitting convection) would change this conclusion.

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