Interdecadal Variations in AGCM Simulation Skills

2006 ◽  
Vol 19 (14) ◽  
pp. 3406-3419 ◽  
Author(s):  
Alice M. Grimm ◽  
A. K. Sahai ◽  
Chester F. Ropelewski
Keyword(s):  
Climate Change ◽  
Climate Variability ◽  
Climate Models ◽  
Lower Boundary ◽  
Climate Prediction ◽  
Global Climate Models ◽  
Future Climate Change ◽  
Model Assessment ◽  
Seasonal Climate ◽  
Seasonal Climate Prediction

Abstract Global climate models forced by sea surface temperature are standard tools in seasonal climate prediction and in projection of future climate change caused by anthropogenic emissions of greenhouse gases. Assessing the ability of these models to reproduce observed atmospheric circulation given the lower boundary conditions, and thus its ability to predict climate, has been a recurrent concern. Several assessments have shown that the performance of models is seasonally dependent, but there has always been the assumption that, for a given season, the model skill is constant throughout the period being analyzed. Here, it is demonstrated that there are periods when these models perform well and periods when they do not capture observed climate variability. The variations of the model performance have temporal scales and spatial patterns consistent with decadal/interdecadal climate variability. These results suggest that there are unmodeled climate processes that affect seasonal climate prediction as well as scenarios of climate change, particularly regional climate change projections. The reliability of these scenarios may depend on the time slice of the model output being analyzed. Therefore, more comprehensive model assessment should include a verification of the long-term stability of their performance.

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 Assessment of extreme flows and uncertainty under climate change: disentangling the uncertainty contribution of representative concentration pathways, global climate models and internal climate variability

2020 ◽  
Vol 24 (6) ◽  
pp. 3251-3269 ◽  
Author(s):  
Chao Gao ◽  
Martijn J. Booij ◽  
Yue-Ping Xu
Keyword(s):  
Climate Change ◽  
Climate Variability ◽  
River Basin ◽  
Climate Models ◽  
Global Climate ◽  
Global Climate Models ◽  
Internal Climate Variability ◽  
Low Flows ◽  
Uncertainty Sources ◽  
High Flows

Abstract. Projections of streamflow, particularly of extreme flows under climate change, are essential for future water resources management and the development of adaptation strategies to floods and droughts. However, these projections are subject to uncertainties originating from different sources. In this study, we explored the possible changes in future streamflow, particularly for high and low flows, under climate change in the Qu River basin, eastern China. ANOVA (analysis of variance) was employed to quantify the contribution of different uncertainty sources from RCPs (representative concentration pathways), GCMs (global climate models) and internal climate variability, using an ensemble of 4 RCP scenarios, 9 GCMs and 1000 simulated realizations of each model–scenario combination by SDRM-MCREM (a stochastic daily rainfall model coupling a Markov chain model with a rainfall event model). The results show that annual mean flow and high flows are projected to increase and that low flows will probably decrease in 2041–2070 (2050s) and 2071–2100 (2080s) relative to the historical period of 1971–2000, suggesting a higher risk of floods and droughts in the future in the Qu River basin, especially for the late 21st century. Uncertainty in mean flows is mostly attributed to GCM uncertainty. For high flows and low flows, internal climate variability and GCM uncertainty are two major uncertainty sources for the 2050s and 2080s, while for the 2080s, the effect of RCP uncertainty becomes more pronounced, particularly for low flows. The findings in this study can help water managers to become more knowledgeable about and get a better understanding of streamflow projections and support decision making regarding adaptations to a changing climate under uncertainty in the Qu River basin.

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.

scholarly journals Peatlands and the carbon cycle: from local processes to global implications – a synthesis

2008 ◽  
Vol 5 (5) ◽  
pp. 1475-1491 ◽  
Author(s):  
J. Limpens ◽  
F. Berendse ◽  
C. Blodau ◽  
J. G. Canadell ◽  
C. Freeman ◽  
...  
Keyword(s):  
Climate Change ◽  
Land Surface ◽  
Climate Models ◽  
Global Climate ◽  
Global Climate Models ◽  
Trace Gas ◽  
Future Climate Change ◽  
Small Scale ◽  
Ecosystem Structure ◽  
Positive Feedbacks

Abstract. Peatlands cover only 3% of the Earth's land surface but boreal and subarctic peatlands store about 15–30% of the world's soil carbon (C) as peat. Despite their potential for large positive feedbacks to the climate system through sequestration and emission of greenhouse gases, peatlands are not explicitly included in global climate models and therefore in predictions of future climate change. In April 2007 a symposium was held in Wageningen, the Netherlands, to advance our understanding of peatland C cycling. This paper synthesizes the main findings of the symposium, focusing on (i) small-scale processes, (ii) C fluxes at the landscape scale, and (iii) peatlands in the context of climate change. The main drivers controlling C fluxes are largely scale dependent and most are related to some aspects of hydrology. Despite high spatial and annual variability in Net Ecosystem Exchange (NEE), the differences in cumulative annual NEE are more a function of broad scale geographic location and physical setting than internal factors, suggesting the existence of strong feedbacks. In contrast, trace gas emissions seem mainly controlled by local factors. Key uncertainties remain concerning the existence of perturbation thresholds, the relative strengths of the CO2 and CH4 feedback, the links among peatland surface climate, hydrology, ecosystem structure and function, and trace gas biogeochemistry as well as the similarity of process rates across peatland types and climatic zones. Progress on these research areas can only be realized by stronger co-operation between disciplines that address different spatial and temporal scales.

Evaluation and application of Bayesian multi-model estimation in temperature simulations

2013 ◽  
Vol 37 (6) ◽  
pp. 727-744 ◽  
Author(s):  
Chiyuan Miao ◽  
Qingyun Duan ◽  
Qiaohong Sun ◽  
Jianduo Li
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Global Climate Models ◽  
Ensemble Prediction ◽  
Future Climate Change ◽  
Northern Asia ◽  
Decadal Scale ◽  
Model Ensembles

Use of multi-model ensembles from global climate models to simulate the current and future climate change has flourished as a research topic during recent decades. This paper assesses the performance of multi-model ensembles in simulating global land temperature from 1960 to 1999, using Nash-Sutcliffe model efficiency and Taylor diagrams. The future trends of temperature for different scales and emission scenarios are projected based on the posterior model probabilities estimated by Bayesian methods. The results show that ensemble prediction can improve the accuracy of simulations of the spatiotemporal distribution of global temperature. The performance of Bayesian model averaging (BMA) at simulating the annual temperature dynamic is significantly better than single climate models and their simple model averaging (SMA). However, BMA simulation can demonstrate the temperature trend on the decadal scale, but its annual assessment of accuracy is relatively weak. The ensemble prediction presents dissimilarly accurate descriptions in different regions, and the best performance appears in Australia. The results also indicate that future temperatures in northern Asia rise with the greatest speed in some scenarios, and Australia is the most sensitive region for the effects of greenhouse gas emissions. In addition to the uncertainty of ensemble prediction, the impacts of climate change on agriculture production and water resources are discussed as an extension of this research.

Predicting climate change effects on subarctic–Arctic populations of Atlantic salmon (Salmo salar)

2013 ◽  
Vol 70 (2) ◽  
pp. 159-168 ◽  
Author(s):  
Richard D. Hedger ◽  
Line E. Sundt-Hansen ◽  
Torbjørn Forseth ◽  
Ola Ugedal ◽  
Ola H. Diserud ◽  
...  
Keyword(s):  
Climate Change ◽  
Atlantic Salmon ◽  
Salmo Salar ◽  
Climate Models ◽  
Global Climate Models ◽  
Future Climate ◽  
The Arctic ◽  
Future Climate Change ◽  
Northern Norway ◽  
Atlantic Salmon Salmo Salar

We predict an increase in parr recruitment and smolt production of Atlantic salmon (Salmo salar) populations along a climate gradient from the subarctic to the Arctic in western and northern Norway in response to future climate change. Firstly, we predicted local stream temperature and discharge from downscaled data obtained from Global Climate Models. Then, we developed a spatially explicit individual-based model (IBM) parameterized for the freshwater stage, with combinations of three different postsmolt survival probabilities reflecting different marine survival regimes. The IBM was run for three locations: southern Norway (∼59°N), western Norway (∼62°N), and northern Norway (∼70°N). Increased temperatures under the future climate regimes resulted in faster parr growth, earlier smolting, and elevated smolt production in the western and northern locations, in turn leading to increased egg deposition and elevated recruitment into parr. In the southern location, density-dependent mortality of parr resulting from low summer wetted-areas reduced predicted future smolt production in comparison to the other locations. It can be inferred, therefore, that climate change may have both positive and negative effects on anadromous fish abundance within the subarctic–Arctic according to geographical region.

scholarly journals Ecological niche modelling predicts significant impacts of future climate change on two endemic rodents in eastern Africa

2021 ◽  
Vol 13 (5) ◽  
pp. 18164-18176
Author(s):  
Aditya Srinivasulu ◽  
Alembrhan Assefa ◽  
Chelmala Srinivasulu
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Global Climate ◽  
Global Climate Models ◽  
Eastern Africa ◽  
Future Climate Change ◽  
Niche Modelling ◽  
Climatic Scenarios ◽  
Bioclimatic Conditions ◽  
The Impact

The impact of climate change on rodents is well studied, however, many of these studies are restricted to the Americas.  Small- to medium-sized rodents, especially murids, are restricted in their home range and microclimatic niche breadth, and are known to be more sensitive to changes in bioclimatic conditions over time.  We analyzed the effect of future climatic scenarios in the near and distant future, using two global climate models (CanESM5 and MIROC-ES2L) for two shared socio-economic pathways (SSP2-4.5 and SSP5-8.5), on two eastern Africa endemic small-bodied mice: Stenocephalemys albipes and Mastomys awashensis. Our results indicate that while S. albipes showed increases in area of climatic suitability in the future, M. awashensis is predicted to suffer severe decline in the area of its fundamental niche.    

scholarly journals Drought Hazard Evaluation in Boro Paddy Cultivated Areas of Western Bangladesh at Current and Future Climate Change Conditions

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Abu Reza Md. Towfiqul Islam ◽  
Shuanghe Shen ◽  
Zhenghua Hu ◽  
M. Atiqur Rahman
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Potential Evapotranspiration ◽  
Hazard Index ◽  
Global Climate Models ◽  
Future Climate ◽  
Future Climate Change ◽  
Hazard Evaluation ◽  
Drought Hazard ◽  
Northwestern Region

Drought hazard is one of the main hindrances for sustaining food security in Bangladesh, and climate change may exacerbate it in the next several decades. This study aims to evaluate drought hazard at current and future climate change conditions in theBoropaddy cultivated areas of western Bangladesh using simulated climate data from the outputs of three global climate models (GCMs) based on the SRES A1B scenario for the period between 2041 and 2070. The threshold level of Standardized Precipitation Evapotranspiration Index (SPEI) was employed to identify drought events and its probability distribution function (PDF) was applied to create the drought hazard index. The study demonstrates that enhancement of potential evapotranspiration (PET) will surpass that of precipitation, resulting in intensified drought events in future. In addition, the PDFs of drought events will move the upper tail in future period compared to the baseline. The results showed that the southwestern region was more severe to the drought hazard than the northwestern region during the period of 1984 to 2013. From the results of three GCMs, in the mid-century period, drought hazard will slightly increase in the northwestern region and flatten with a decrease in the southwestern region. The outcomes will help to allocate agricultural adaptation plans under climate change condition in Bangladesh.

Going with the trend: forecasting seasonal climate conditions under climate change

2021 ◽  
Author(s):  
Yawen Shao ◽  
Quan J. Wang ◽  
Andrew Schepen ◽  
Dongryeol Ryu
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Models ◽  
Daily Maximum ◽  
Post Processing ◽  
Seasonal Forecasts ◽  
Climate Conditions ◽  
Seasonal Climate

AbstractFor managing climate variability and adapting to climate change, seasonal forecasts are widely produced to inform decision making. However, seasonal forecasts from global climate models are found to poorly reproduce temperature trends in observations. Furthermore, this problem is not addressed by existing forecast post-processing methods that are needed to remedy biases and uncertainties in model forecasts. The inability of the forecasts to reproduce the trends severely undermines user confidence in the forecasts. In our previous work, we proposed a new statistical post-processing model that counteracted departures in trends of model forecasts from observations. Here, we further extend this trend-aware forecast post-processing methodology to carefully treat the trend uncertainty associated with the sampling variability due to limited data records. This new methodology is validated on forecasting seasonal averages of daily maximum and minimum temperatures for Australia based on the SEAS5 climate model of the European Centre for Medium-Range Weather Forecasts. The resulting post-processed forecasts are shown to have proper trends embedded, leading to greater accuracy in regions with significant trends. The application of this new forecast post-processing is expected to boost user confidence in seasonal climate forecasts.

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