scholarly journals Intensification characteristics of hydroclimatic extremes in the Asia monsoon region under 1.5 and 2.0 °C of global warming

2020 ◽  
Author(s):  
Jeong-Bae Kim ◽  
Deg-Hyo Bae
Keyword(s):  
Global Warming ◽  
Climate Models ◽  
Global Climate ◽  
Regional Climate ◽  
Global Climate Models ◽  
Monsoon Region ◽  
Infiltration Capacity ◽  
Meteorological Forcing ◽  
Climate Zones ◽  
Global Mean Temperature

Abstract. The changes in hydroclimatic extremes are assessed over the Asia monsoon region under 1.5 and 2.0 °C warming targets of global mean temperature above preindustrial levels based on a representative concentration pathway (RCP) 4.5 scenario. The subregions in this domain are defined by the Köppen climate classification method to identify regional climate characteristics. The change patterns of long-term hydroclimatic mean and hydroclimatic extreme among subregions are compared based on the multimodel ensemble (MME) of selected five global climate models (GCMs). Each GCM is bias corrected and then used as a meteorological forcing for a hydrological model. To simulate how the hydrologic system responds to 1.5 and 2.0 °C global warming targets, we select the variable infiltration capacity (VIC) model. The results of temperature extremes show significant change patterns over all climate zones. As the globe warms, the increasing warm extremes and the decreasing cold extremes with a high robustness occur more frequently over Asia. Meanwhile, changes in precipitation and runoff averages (and low runoff extremes) show large spatial variations in change patterns with little robustness based on intermodel agreement. Global warming is expected to significantly intensify maximum precipitation extremes in all climate zones. Regardless of regional climate characteristics, this behavior is expected to be enhanced under 2.0 °C compare to 1.5 °C warming scenario and cause the likelihood of flood risk. The spatial extent and magnitude of change patterns in runoff are modulated by those of change patterns in precipitation. More importantly, an extra 0.5 °C of global warming also leads to amplified change signals and more robust change patterns in hydroclimatic extremes, especially in cold (and polar) climate zones. The results of this study demonstrate that the clear changes in regional hydroclimatic extremes under warmer conditions over Asia, and hydroclimatic sensitivities differ based on regional climate characteristics.

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.

Projected precipitation changes over China for global warming levels at 1.5°C and 2°C in an ensemble of regional climate simulations: Impact of bias-correction algorithms

2020 ◽  
Author(s):  
Lianyi Guo
Keyword(s):  
Global Warming ◽  
Bias Correction ◽  
Climate Models ◽  
Dynamical Downscaling ◽  
Comprehensive Evaluation ◽  
Global Climate ◽  
Regional Climate ◽  
Global Climate Models ◽  
Cumulative Distribution ◽  
Mathematical Framework

<p>Four bias-correction methods, i.e. Gamma Cumulative Distribution Function (GamCDF), Quantile-Quantile Adjustment (QQadj), Equidistant CDF Matching (EDCDF) and Transform CDF (CDF-t), were applied to five daily precipitation datasets over China produced by LMDZ4-regional that was nested into five global climate models (GCMs), BCC-CSM1-1m, CNRM-CM5, FGOALS-g2, IPSL-CM5A-MR and MPI-ESM-MR, respectively. A unified mathematical framework can be used to define the four methods, which helps understanding their nature and essence in identifying the most reliable probability distributions of projected climate. CDF-t is shown to be the best bias-correction algorithm based on a comprehensive evaluation of different rainfall indices. Future precipitation projections corresponds to the global warming levels of 1.5°C and 2°C under RCP8.5 were obtained using the bias correction methods. The multi-algorithm and multi-model ensemble characteristics allow to explore the spreading of results, considered as a surrogate of climate projection uncertainty, and to attribute such uncertainties to different sources. It was found that the spread among bias-correction methods is smaller than that among dynamical downscaling simulations. The four bias-correction methods with CDF-t at the top all reduce the spread among the downscaled results. Future projection using CDF-t is thus considered having higher credibility.</p>

scholarly journals Intensification characteristics of hydroclimatic extremes in the Asian monsoon region under 1.5 and 2.0 °C of global warming

2020 ◽  
Vol 24 (12) ◽  
pp. 5799-5820
Author(s):  
Jeong-Bae Kim ◽  
Deg-Hyo Bae
Keyword(s):  
Global Warming ◽  
Asian Monsoon ◽  
Regional Climate ◽  
Global Climate Models ◽  
Mean Annual Precipitation ◽  
Monsoon Region ◽  
Asian Monsoon Region ◽  
Climate Zones ◽  
Extreme Precipitation Events ◽  
Strong Robustness

Abstract. Understanding the influence of global warming on regional hydroclimatic extremes is challenging. To reduce the potential risk of extremes under future climate states, assessing the change in extreme climate events is important, especially in Asia, due to spatial variability of climate and its seasonal variability. Here, the changes in hydroclimatic extremes are assessed over the Asian monsoon region under global mean temperature warming targets of 1.5 and 2.0 ∘C above preindustrial levels based on representative concentration pathways (RCPs) 4.5 and 8.5. Analyses of the subregions classified using regional climate characteristics are performed based on the multimodel ensemble mean (MME) of five bias-corrected global climate models (GCMs). For runoff extremes, the hydrologic responses to 1.5 and 2.0 ∘C global warming targets are simulated based on the variable infiltration capacity (VIC) model. Changes in temperature extremes show increasing warm extremes and decreasing cold extremes in all climate zones with strong robustness under global warming conditions. However, the hottest extreme temperatures occur more frequently in low-latitude regions with tropical climates. Changes in mean annual precipitation and mean annual runoff and low-runoff extremes represent the large spatial variations with weak robustness based on intermodel agreements. Global warming is expected to consistently intensify maximum extreme precipitation events (usually exceeding a 10 % increase in intensity under 2.0 ∘C of warming) in all climate zones. The precipitation change patterns directly contribute to the spatial extent and magnitude of the high-runoff extremes. Regardless of regional climate characteristics and RCPs, this behavior is expected to be enhanced under the 2.0 ∘C (compared with the 1.5 ∘C) warming scenario and increase the likelihood of flood risk (up to 10 %). More importantly, an extra 0.5 ∘C of global warming under two RCPs will amplify the change in hydroclimatic extremes on temperature, precipitation, and runoff with strong robustness, especially in cold (and polar) climate zones. The results of this study clearly show the consistent changes in regional hydroclimatic extremes related to temperature and high precipitation and suggest that hydroclimatic sensitivities can differ based on regional climate characteristics and type of extreme variables under warmer conditions over Asia.

scholarly journals Robust increase of Indian monsoon rainfall and its variability under future warming in CMIP6 models

2021 ◽  
Vol 12 (2) ◽  
pp. 367-386
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 ◽  
Cmip5 Models ◽  
Future Climate Change ◽  
Global Mean Temperature

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 (CMIP5) indicated a consistent increase in monsoon rainfall and its variability under global warming. Since the range of the results of CMIP5 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 CMIP6 are of interest. Here, we analyze 32 models of the latest CMIP6 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 a high agreement between the models independent of the SSP if global warming is the dominant forcing of the monsoon dynamics as it is in the 21st century; the multi-model mean for JJAS projects an increase of 0.33 mm d−1 and 5.3 % per kelvin of global warming. This is significantly higher than in the CMIP5 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 CMIP6 simulations largely confirm the findings from CMIP5 models, but show an increased robustness across models with reduced uncertainties and updated magnitudes towards a stronger increase in monsoon rainfall.

scholarly journals The Atmospheric Pathway of the Cloud-Radiative Impact on the Circulation Response to Global Warming: Important and Uncertain

2019 ◽  
Vol 32 (10) ◽  
pp. 3051-3067 ◽  
Author(s):  
Aiko Voigt ◽  
Nicole Albern ◽  
Georgios Papavasileiou
Keyword(s):  
Global Warming ◽  
Climate Models ◽  
Global Climate ◽  
Regional Climate ◽  
Regional Climate Change ◽  
Global Climate Models ◽  
Radiative Heating ◽  
Substantial Part ◽  
Radiative Impact ◽  
High Level

Abstract Previous work showed that the poleward expansion of the annual-mean zonal-mean atmospheric circulation in response to global warming is strongly modulated by changes in clouds and their radiative heating of the surface and atmosphere. Here, a hierarchy and an ensemble of global climate models are used to study the circulation impact of changes in atmospheric cloud-radiative heating in the absence of changes in sea surface temperature (SST), which is referred to as the atmospheric pathway of the cloud-radiative impact. For the MPI-ESM model, the atmospheric pathway is responsible for about half of the total cloud-radiative impact, and in fact half of the total circulation response. Changes in atmospheric cloud-radiative heating are substantial in both the lower and upper troposphere. However, because SST is prescribed the atmospheric pathway is dominated by changes in upper-tropospheric cloud-radiative heating, which in large part results from the upward shift of high-level clouds. The poleward circulation expansion via the atmospheric pathway and changes in upper-tropospheric cloud-radiative heating are qualitatively robust across three global models, yet their magnitudes vary by a factor of 3. A substantial part of these magnitude differences are related to the upper-tropospheric radiative heating by high-level clouds in the present-day climate. A comparison with observations highlights the model deficits in representing the radiative heating by high-level clouds and indicates that reducing these deficits can contribute to improved predictions of regional climate change.

scholarly journals Regions of intensification of extreme snowfall under future warming

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Lennart Quante ◽  
Sven N. Willner ◽  
Robin Middelanis ◽  
Anders Levermann
Keyword(s):  
Climate Change ◽  
Global Warming ◽  
Observational Data ◽  
Climate Models ◽  
Hydrological Cycle ◽  
Global Climate ◽  
Global Climate Models ◽  
Average Intensity ◽  
High Latitudes ◽  
Future Warming

AbstractDue to climate change the frequency and character of precipitation are changing as the hydrological cycle intensifies. With regards to snowfall, global warming has two opposing influences; increasing humidity enables intense snowfall, whereas higher temperatures decrease the likelihood of snowfall. Here we show an intensification of extreme snowfall across large areas of the Northern Hemisphere under future warming. This is robust across an ensemble of global climate models when they are bias-corrected with observational data. While mean daily snowfall decreases, both the 99th and the 99.9th percentiles of daily snowfall increase in many regions in the next decades, especially for Northern America and Asia. Additionally, the average intensity of snowfall events exceeding these percentiles as experienced historically increases in many regions. This is likely to pose a challenge to municipalities in mid to high latitudes. Overall, extreme snowfall events are likely to become an increasingly important impact of climate change in the next decades, even if they will become rarer, but not necessarily less intense, in the second half of the century.

scholarly journals Performance of SEACLID/CORDEX-SEA multi-model experiments in simulating temperature and rainfall in Vietnam

2019 ◽  
Vol 41 (4) ◽  
pp. 374-387 ◽  
Author(s):  
Nguyen Thi Tuyet ◽  
Ngo Duc Thanh ◽  
Phan Van Tan
Keyword(s):  
Southeast Asia ◽  
Climate Models ◽  
Global Climate ◽  
Regional Climate ◽  
Global Climate Models ◽  
Red River ◽  
Observation Data ◽  
Red River Delta ◽  
North Central ◽  
Climate Downscaling

The study examined the performance of six regional climate experiments conducted under the framework of the Southeast Asia Regional Climate Downscaling/Coordinated Regional Climate Downscaling Experiment-Southeast Asia (SEACLID/CORDEX-SEA) project and their ensemble product (ENS) in simulating temperature at 2 m (T2m) and rainfall (R) in seven climatic sub-regions of Vietnam. The six experiments were named following the names of their driving Global Climate Models (GCMs), i.e., CNRM, CSIRO, ECEA, GFDL, HADG and MPI. The observation data for the period 1986–2005 from 66 stations in Vietnam were used to compare with the model outputs. Results showed that cold biases were prominent among the experiments and ENS well reproduced the seasonal cycle of temperature in the Northeast, Red River Delta, North Central and Central Highlands regions. For rainfall, all the experiments showed wet biases and CSIRO exhibited the best. A scoring system was elaborated to objectively rank the performance of the experiments and the ENS experiment was reported to be the best.

A Bayesian Hierarchical Model for Heterogeneous RCM–GCM Multimodel Ensembles*

2015 ◽  
Vol 28 (15) ◽  
pp. 6249-6266 ◽  
Author(s):  
Christian Kerkhoff ◽  
Hans R. Künsch ◽  
Christoph Schär
Keyword(s):  
Hierarchical Model ◽  
Climate Models ◽  
Global Climate ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Bayesian Hierarchical Model ◽  
Global Climate Models ◽  
Seasonal Temperature ◽  
Bayesian Hierarchical ◽  
Multimodel Ensembles

Abstract A Bayesian hierarchical model for heterogeneous multimodel ensembles of global and regional climate models is presented. By applying the methodology herein to regional and seasonal temperature averages from the ENSEMBLES project, probabilistic projections of future climate are derived. Intermodel correlations that are particularly strong between regional climate models and their driving global climate models are explicitly accounted for. Instead of working with time slices, a data archive is investigated in a transient setting. This enables a coherent treatment of internal variability on multidecadal time scales. Results are presented for four European regions to highlight the feasibility of the approach. In particular, the methodology is able to objectively identify patterns of variability changes, in ways that previously required subjective expert knowledge. Furthermore, this study underlines that assumptions about bias changes have an effect on the projected warming. It is also shown that validating the out-of-sample predictive performance is possible on short-term prediction horizons and that the hierarchical model herein is competitive. Additionally, the findings indicate that instead of running a large suite of regional climate models all forced by the same driver, priority should be given to a rich diversity of global climate models that force a number of regional climate models in the experimental design of future multimodel ensembles.

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.

scholarly journals Increased water vapour lifetime due to global warming

2019 ◽  
Author(s):  
Øivind Hodnebrog ◽  
Gunnar Myhre ◽  
Bjørn H. Samset ◽  
Kari Alterskjær ◽  
Timothy Andrews ◽  
...  
Keyword(s):  
Global Warming ◽  
Water Vapour ◽  
Solar Irradiance ◽  
Climate Models ◽  
Hydrological Cycle ◽  
Global Climate ◽  
Global Climate Models ◽  
Climate Projections ◽  
Surface Temperature Change ◽  
Surface Warming

Abstract. The relationship between changes in integrated water vapour (IWV) and precipitation can be characterized by quantifying changes in atmospheric water vapour lifetime. Precipitation isotope ratios correlate with this lifetime, a relationship that helps understand dynamical processes and may lead to improved climate projections. We investigate how water vapour and its lifetime respond to different drivers of climate change, such as greenhouse gases and aerosols. Results from 11 global climate models have been used, based on simulations where CO2, methane, solar irradiance, black carbon (BC), and sulphate have been perturbed separately. A lifetime increase from 8 to 10 days is projected between 1986–2005 and 2081–2100, under a business-as-usual pathway. By disentangling contributions from individual climate drivers, we present a physical understanding of how global warming slows down the hydrological cycle, due to longer lifetime, but still amplifies the cycle due to stronger precipitation/evaporation fluxes. The feedback response of IWV to surface temperature change differs somewhat between drivers. Fast responses amplify these differences and lead to net changes in IWV per degree surface warming ranging from 6.4±0.9 %/K for sulphate to 9.8±2 %/K for BC. While BC is the driver with the strongest increase in IWV per degree surface warming, it is also the only driver with a reduction in precipitation per degree surface warming. Consequently, increases in BC aerosol concentrations yield the strongest slowdown of the hydrological cycle among the climate drivers studied, with a change in water vapour lifetime per degree surface warming of 1.1±0.4 days/K, compared to less than 0.5 days/K for the other climate drivers (CO2, methane, solar irradiance, sulphate).

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