scholarly journals Hydrologic Alterations Predicted by Seasonally-Consistent Subset Ensembles of General Circulation Models

Climate ◽  
2017 ◽  
Vol 5 (3) ◽  
pp. 44 ◽  
Author(s):  
Aleksey Sheshukov ◽  
Kyle Douglas-Mankin
Keyword(s):  
General Circulation ◽  
General Circulation Models ◽  
Consistent Subset ◽  
Hydrologic Alterations

scholarly journals Hydrologic alteration and potential ecosystemic implications under a changing climate in the Chitral River, Hindukush region, Pakistan

2020 ◽  
Author(s):  
M. Usman ◽  
X. Pan ◽  
D. Penna ◽  
B. Ahmad
Keyword(s):  
Climate Change ◽  
General Circulation ◽  
Bird Species ◽  
General Circulation Models ◽  
Migratory Bird ◽  
Historical Period ◽  
Hydrologic Alteration ◽  
Study Region ◽  
Assess Climate Change ◽  
Hydrologic Alterations

Abstract This study investigates changes in the hydrologic regime of the Chitral River, Hindukush–Karakoram–Himalayan (HKH) region, Pakistan. Different statistically based methods were used to assess climate change-induced hydrologic alterations that can possibly impact aquatic habitat in the study region. The hydrological model Hydrologiska Byråns Vattenbalansavdelning (HBV) was calibrated, validated, and applied to predict streamflow in the Chitral River basin. The HBV model was forced with the ensemble of four general circulation models under different representative concentration pathway emission scenarios to generate future streamflow under climate change conditions in the basin for the mid-twenty-first century. The results of this study show that hydrologic regimes in the study area, expressed by the magnitude, duration, frequency, timing, and rate of streamflow, are likely to alter in the future. Positive (i.e., with increased frequency) hydrologic alteration is projected for most flow parameters under all scenarios for the 2021–2050 period compared with values observed during the historical period (1976–2005). These hydrologic alterations might have impacts on fish and migratory bird species in the study area. This research can be helpful in providing practical information for more effective water resources and aquatic ecosystem management in the HKH region.

Successive Modulation of ENSO to the Future Greenhouse Warming

2008 ◽  
Vol 21 (1) ◽  
pp. 3-21 ◽  
Author(s):  
Soon-Il An ◽  
Jong-Seong Kug ◽  
Yoo-Geun Ham ◽  
In-Sik Kang
Keyword(s):  
General Circulation ◽  
Southern Oscillation ◽  
General Circulation Models ◽  
Tropical Pacific ◽  
Greenhouse Warming ◽  
Delayed Response ◽  
Subsurface Temperature ◽  
Climate System Model ◽  
The Mean ◽  
The Tropical Pacific

Abstract The multidecadal modulation of the El Niño–Southern Oscillation (ENSO) due to greenhouse warming has been analyzed herein by means of diagnostics of Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) coupled general circulation models (CGCMs) and the eigenanalysis of a simplified version of an intermediate ENSO model. The response of the global-mean troposphere temperature to increasing greenhouse gases is more likely linear, while the amplitude and period of ENSO fluctuates in a multidecadal time scale. The climate system model outputs suggest that the multidecadal modulation of ENSO is related to the delayed response of the subsurface temperature in the tropical Pacific compared to the response time of the sea surface temperature (SST), which would lead a modulation of the vertical temperature gradient. Furthermore, an eigenanalysis considering only two parameters, the changes in the zonal contrast of the mean background SST and the changes in the vertical contrast between the mean surface and subsurface temperatures in the tropical Pacific, exhibits a good agreement with the CGCM outputs in terms of the multidecadal modulations of the ENSO amplitude and period. In particular, the change in the vertical contrast, that is, change in difference between the subsurface temperature and SST, turns out to be more influential on the ENSO modulation than changes in the mean SST itself.

scholarly journals Contributors to linkage between Arctic warming and East Asian winter climate

2021 ◽  
Author(s):  
Xinping Xu ◽  
Shengping He ◽  
Yongqi Gao ◽  
Botao Zhou ◽  
Huijun Wang
Keyword(s):  
Sea Ice ◽  
General Circulation ◽  
Initial Conditions ◽  
Wave Train ◽  
East Asian ◽  
Surface Air Temperature ◽  
General Circulation Models ◽  
The Arctic ◽  
Winter Climate ◽  
Arctic Warming

AbstractPrevious modelling and observational studies have shown discrepancies in the interannual relationship of winter surface air temperature (SAT) between Arctic and East Asia, stimulating the debate about whether Arctic change can influence midlatitude climate. This study uses two sets of coordinated experiments (EXP1 and EXP2) from six different atmospheric general circulation models. Both EXP1 and EXP2 consist of 130 ensemble members, each of which in EXP1 (EXP2) was forced by the same observed daily varying sea ice and daily varying (daily climatological) sea surface temperature (SST) for 1982–2014 but with different atmospheric initial conditions. Large spread exists among ensemble members in simulating the Arctic–East Asian SAT relationship. Only a fraction of ensemble members can reproduce the observed deep Arctic warming–cold continent pattern which extends from surface to upper troposphere, implying the important role of atmospheric internal variability. The mechanisms of deep Arctic warming and shallow Arctic warming are further distinguished. Arctic warming aloft is caused primarily by poleward moisture transport, which in conjunction with the surface warming coupled with sea ice melting constitutes the surface-amplified deep Arctic warming throughout the troposphere. These processes associated with the deep Arctic warming may be related to the forcing of remote SST when there is favorable atmospheric circulation such as Rossby wave train propagating from the North Atlantic into the Arctic.

scholarly journals Simulation of Extreme Precipitation in Four Climate Regions in China by General Circulation Models (GCMs): Performance and Projections

Water ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 1509
Author(s):  
Mengru Zhang ◽  
Xiaoli Yang ◽  
Liliang Ren ◽  
Ming Pan ◽  
Shanhu Jiang ◽  
...  
Keyword(s):  
Extreme Precipitation ◽  
General Circulation ◽  
Global Climate ◽  
Summer Precipitation ◽  
General Circulation Models ◽  
Cumulative Distribution ◽  
Extreme Precipitation Events ◽  
Increasing Trend ◽  
The Future ◽  
Semi Arid

In the context of global climate change, it is important to monitor abnormal changes in extreme precipitation events that lead to frequent floods. This research used precipitation indices to describe variations in extreme precipitation and analyzed the characteristics of extreme precipitation in four climatic (arid, semi-arid, semi-humid and humid) regions across China. The equidistant cumulative distribution function (EDCDF) method was used to downscale and bias-correct daily precipitation in eight Coupled Model Intercomparison Project Phase 5 (CMIP5) general circulation models (GCMs). From 1961 to 2005, the humid region had stronger and longer extreme precipitation compared with the other regions. In the future, the projected extreme precipitation is mainly concentrated in summer, and there will be large areas with substantial changes in maximum consecutive 5-day precipitation (Rx5) and precipitation intensity (SDII). The greatest differences between two scenarios (RCP4.5 and RCP8.5) are in semi-arid and semi-humid areas for summer precipitation anomalies. However, the area of the four regions with an increasing trend of extreme precipitation is larger under the RCP8.5 scenario than that under the RCP4.5 scenario. The increasing trend of extreme precipitation in the future is relatively pronounced, especially in humid areas, implying a potential heightened flood risk in these areas.

scholarly journals Dynamics of potential precipitation under climate change scenarios at Cameron highlands, Malaysia

2021 ◽  
Vol 3 (3) ◽  
Author(s):  
Nuraddeen Mukhtar Nasidi ◽  
Aimrun. Wayayok ◽  
Ahmad Fikri Abdullah ◽  
Muhamad Saufi Mohd Kassim
Keyword(s):  
Environmental Sustainability ◽  
General Circulation ◽  
Greenhouse Gas Emission ◽  
Rapid Change ◽  
Absolute Error ◽  
General Circulation Models ◽  
Coefficient Of Determination ◽  
Climate Change Scenarios ◽  
Precipitation Concentration Index ◽  
Cameron Highlands

AbstractPrecipitation is sensitive to increasing greenhouse gas emission which has a significant impact on environmental sustainability. Rapid change of climate variables is often result into large variation in rainfall characteristics which trigger other forms of hazards such as floods, erosion, and landslides. This study employed multi-model ensembled general circulation models (GCMs) approach to project precipitation into 2050s and 2080s periods under four RCPs emission scenarios. Spatial analysis was performed in ArcGIS10.5 environment using Inverse Distance Weighted (IDW) interpolation and Arc-Hydro extension. The model validation indicated by coefficient of determination, Nash–Sutcliffe efficiency, percent bias, root mean square error, standard error, and mean absolute error are 0.73, 0.27, 20.95, 1.25, 0.37 and 0.15, respectively. The results revealed that the Cameron Highlands will experience higher mean daily precipitations between 5.4 mm in 2050s and 9.6 mm in 2080s under RCP8.5 scenario, respectively. Analysis of precipitation concentration index (PCI) revealed that 75% of the watershed has PCI greater than 20 units which indicates substantial variability of the precipitation. Similarly, there is varied spatial distribution patterns of projected precipitation over the study watershed with the largest annual values ranged between 2900 and 3000 mm, covering 71% of the total area in 2080s under RCP8.5 scenario. Owing to this variability in rainfall magnitudes, appropriate measures for environmental protection are essential and to be strategized to address more vulnerable areas.

scholarly journals Review on Applications of 17O in Hydrological Cycle

Molecules ◽  
2021 ◽  
Vol 26 (15) ◽  
pp. 4468
Author(s):  
Yalalt Nyamgerel ◽  
Yeongcheol Han ◽  
Minji Kim ◽  
Dongchan Koh ◽  
Jeonghoon Lee
Keyword(s):  
Water Vapor ◽  
General Circulation ◽  
Hydrological Cycle ◽  
General Circulation Models ◽  
Convective Precipitation ◽  
Subsurface Water ◽  
Polar Regions ◽  
Sea Ice Concentration ◽  
Moisture Source ◽  
Depositional Processes

The triple oxygen isotopes (16O, 17O, and 18O) are very useful in hydrological and climatological studies because of their sensitivity to environmental conditions. This review presents an overview of the published literature on the potential applications of 17O in hydrological studies. Dual-inlet isotope ratio mass spectrometry and laser absorption spectroscopy have been used to measure 17O, which provides information on atmospheric conditions at the moisture source and isotopic fractionations during transport and deposition processes. The variations of δ17O from the developed global meteoric water line, with a slope of 0.528, indicate the importance of regional or local effects on the 17O distribution. In polar regions, factors such as the supersaturation effect, intrusion of stratospheric vapor, post-depositional processes (local moisture recycling through sublimation), regional circulation patterns, sea ice concentration and local meteorological conditions determine the distribution of 17O-excess. Numerous studies have used these isotopes to detect the changes in the moisture source, mixing of different water vapor, evaporative loss in dry regions, re-evaporation of rain drops during warm precipitation and convective storms in low and mid-latitude waters. Owing to the large variation of the spatial scale of hydrological processes with their extent (i.e., whether the processes are local or regional), more studies based on isotopic composition of surface and subsurface water, convective precipitation, and water vapor, are required. In particular, in situ measurements are important for accurate simulations of atmospheric hydrological cycles by isotope-enabled general circulation models.

scholarly journals The relevance of mid-Holocene Arctic warming to the future

2019 ◽  
Vol 15 (4) ◽  
pp. 1375-1394 ◽  
Author(s):  
Masakazu Yoshimori ◽  
Marina Suzuki
Keyword(s):  
Surface Temperature ◽  
General Circulation ◽  
Temperature Response ◽  
Longwave Radiation ◽  
General Circulation Models ◽  
Cross Model ◽  
Arctic Warming ◽  
Temperature Changes ◽  
The Future ◽  
Arctic Climate Change

Abstract. There remain substantial uncertainties in future projections of Arctic climate change. There is a potential to constrain these uncertainties using a combination of paleoclimate simulations and proxy data, but such a constraint must be accompanied by physical understanding on the connection between past and future simulations. Here, we examine the relevance of an Arctic warming mechanism in the mid-Holocene (MH) to the future with emphasis on process understanding. We conducted a surface energy balance analysis on 10 atmosphere and ocean general circulation models under the MH and future Representative Concentration Pathway (RCP) 4.5 scenario forcings. It is found that many of the dominant processes that amplify Arctic warming over the ocean from late autumn to early winter are common between the two periods, despite the difference in the source of the forcing (insolation vs. greenhouse gases). The positive albedo feedback in summer results in an increase in oceanic heat release in the colder season when the atmospheric stratification is strong, and an increased greenhouse effect from clouds helps amplify the warming during the season with small insolation. The seasonal progress was elucidated by the decomposition of the factors associated with sea surface temperature, ice concentration, and ice surface temperature changes. We also quantified the contribution of individual components to the inter-model variance in the surface temperature changes. The downward clear-sky longwave radiation is one of major contributors to the model spread throughout the year. Other controlling terms for the model spread vary with the season, but they are similar between the MH and the future in each season. This result suggests that the MH Arctic change may not be analogous to the future in some seasons when the temperature response differs, but it is still useful to constrain the model spread in the future Arctic projection. The cross-model correlation suggests that the feedbacks in preceding seasons should not be overlooked when determining constraints, particularly summer sea ice cover for the constraint of autumn–winter surface temperature response.

scholarly journals A Climate Downscaling Deep Learning Model considering the Multiscale Spatial Correlations and Chaos of Meteorological Events

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Bin Mu ◽  
Bo Qin ◽  
Shijin Yuan ◽  
Xiaoyun Qin
Keyword(s):  
Deep Learning ◽  
General Circulation ◽  
Dynamical Downscaling ◽  
General Circulation Models ◽  
Learning Model ◽  
Spatial Correlations ◽  
Reconstruction Process ◽  
Daily Precipitation Data ◽  
Climate Downscaling ◽  
Deep Learning Model

Climate downscaling is a way to provide finer resolution data at local scales, which has been widely used in meteorological research. The two main approaches for climate downscaling are dynamical and statistical. The traditional dynamical downscaling methods are quite time- and resource-consuming based on general circulation models (GCMs). Recently, more and more researchers construct a statistical deep learning model for climate downscaling motivated by the single-image superresolution (SISR) process in computer vision (CV). This is an approach that uses historical climate observations to learn a low-resolution to high-resolution mapping and produces great enhancements in terms of efficiency and effectiveness. Therefore, it has provided an appreciable new insight and successful downscaling solution to multiple climate phenomena. However, most existing models only make a simple analogy between climate downscaling and SISR and ignore the underlying dynamical mechanisms, which leads to the overaveraged downscaling results lacking crucial physical details. In this paper, we incorporate the a priori meteorological knowledge into a deep learning formalization for climate downscaling. More specifically, we consider the multiscale spatial correlations and the chaos in multiple climate events. Depending on two characteristics, we build up a two-stage deep learning model containing a stepwise reconstruction process and ensemble inference, which is named climate downscaling network (CDN). It can extract more local/remote spatial dependencies and provide more comprehensive captures of extreme conditions. We evaluate our model based on two datasets: climate science dataset (CSD) and benchmark image dataset (BID). The results demonstrate that our model shows the effectiveness and superiority in downscaling daily precipitation data from 2.5 degrees to 0.5 degrees over Asia and Europe. In addition, our model exhibits better performance than the other traditional approaches and state-of-the-art deep learning models.

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