Evaluation of future runoff variations in the north–south transect of eastern China: effects of CMIP5 models outputs uncertainty

2019 ◽  
Vol 11 (4) ◽  
pp. 1355-1369 ◽  
Author(s):  
Guodong Sun ◽  
Fei Peng
Keyword(s):  
General Circulation ◽  
Eastern China ◽  
Coupled Model ◽  
General Circulation Models ◽  
Annual Runoff ◽  
Actual Evapotranspiration ◽  
Cmip5 Models ◽  
Climate Projections ◽  
Uncertainty Range ◽  
The North

Abstract Runoff is an important water flux that is difficult to simulate and predict due to lacking observation. Meteorological forcing data are a key factor in causing the uncertainty of predicted runoff. In this study, climate projections from ten general circulation models of the Coupled Model Intercomparison Project 5 (CMIP5) with high resolution under the Representative Concentration Pathway (RCP) 4.5 scenario are employed to estimate the future uncertainty range of predicted runoff in the North–South Transect of Eastern China (NSTEC) from 2011 to 2100. It is found that the range of future annual runoff is from 268.9 mm (Meteorological Research Institute coupled GCM, MRI-CGCM3) to 544.2 mm (Model for Interdisciplinary Research on Climate, MIROC5). The precipitation and the annual actual evapotranspiration are two key factors that affect the variation of runoff. The low annual runoff for the MRI-CGCM3 model may be caused by low precipitation and high annual actual evapotranspiration (466.9 mm). However, the high annual runoff for the MIROC5 may be caused by the high precipitation, although there is high annual actual evapotranspiration (544.2 mm). The above results imply that the forcing data and the model physics are important factors in the numerical simulation and prediction about runoff.

Do CMIP5 models hint at a warmer and wetter India in the 21st century?

2015 ◽  
Vol 7 (2) ◽  
pp. 280-295 ◽  
Author(s):  
Rajib Maity ◽  
Ankit Aggarwal ◽  
Kironmala Chanda
Keyword(s):  
General Circulation ◽  
Winter Season ◽  
Coupled Model ◽  
General Circulation Models ◽  
Cmip5 Models ◽  
Historical Period ◽  
The North ◽  
North Eastern ◽  
Spatio Temporal ◽  
Projected Changes

This study diagnoses the spatio-temporal variation of three major hydroclimatic variables (temperature, precipitation and evaporation) estimated from four general circulation models participating in the Fifth Phase of the Coupled Model Intercomparision Project (CMIP5). Changes in climate regime are analyzed across India for the historical scenario (1850–2005) and for the RCP8.5 scenario (2006–2100). The study provides a relative assessment of projected changes in climatic pattern over different zones in India, broadly divided as southern, Eastern, Western, Central, North-Eastern and Himalayan regions. Monthly data for both the scenarios were obtained, and all the data were re-gridded to a common resolution. All the models show a stronger warming in the future as compared to the historical period. The North-Eastern, Northern and Himalayan regions are likely to be severely affected. Though inconsistencies have been observed among the models, the majority of them predict an increase in precipitation in future, with a major increment in southern cities. The Himalayan belt is expected to receive heavy rainfall in the summer season, with little change in the winter season. Most of the regions are not expected to experience change in evaporation in pre-monsoonal months, but substantial change is expected in some regions during monsoonal and post-monsoonal months.

scholarly journals Comparison of regional characteristics of land precipitation climatology projected by an MRI-AGCM multi-cumulus scheme and multi-SST ensemble with CMIP5 multi-model ensemble projections

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Rui Ito ◽  
Tosiyuki Nakaegawa ◽  
Izuru Takayabu
Keyword(s):  
Climate Change ◽  
High Resolution ◽  
General Circulation ◽  
Annual Precipitation ◽  
Cmip5 Models ◽  
Climate Projections ◽  
The Tibetan Plateau ◽  
Model Ensemble ◽  
Uncertainty Range ◽  
High Performing

AbstractEnsembles of climate change projections created by general circulation models (GCMs) with high resolution are increasingly needed to develop adaptation strategies for regional climate change. The Meteorological Research Institute atmospheric GCM version 3.2 (MRI-AGCM3.2), which is listed in the Coupled Model Intercomparison Project phase 5 (CMIP5), has been typically run with resolutions of 60 km and 20 km. Ensembles of MRI-AGCM3.2 consist of members with multiple cumulus convection schemes and different patterns of future sea surface temperature, and are utilized together with their downscaled data; however, the limited size of the high-resolution ensemble may lead to undesirable biases and uncertainty in future climate projections that will limit its appropriateness and effectiveness for studies on climate change and impact assessments. In this study, to develop a comprehensive understanding of the regional precipitation simulated with MRI-AGCM3.2, we investigate how well MRI-AGCM3.2 simulates the present-day regional precipitation around the globe and compare the uncertainty in future precipitation changes and the change projection itself between MRI-AGCM3.2 and the CMIP5 multiple atmosphere–ocean coupled GCM (AOGCM) ensemble. MRI-AGCM3.2 reduces the bias of the regional mean precipitation obtained with the high-performing CMIP5 models, with a reduction of approximately 20% in the bias over the Tibetan Plateau through East Asia and Australia. When 26 global land regions are considered, MRI-AGCM3.2 simulates the spatial pattern and the regional mean realistically in more regions than the individual CMIP5 models. As for the future projections, in 20 of the 26 regions, the sign of annual precipitation change is identical between the 50th percentiles of the MRI-AGCM3.2 ensemble and the CMIP5 multi-model ensemble. In the other six regions around the tropical South Pacific, the differences in modeling with and without atmosphere–ocean coupling may affect the projections. The uncertainty in future changes in annual precipitation from MRI-AGCM3.2 partially overlaps the maximum–minimum uncertainty range from the full ensemble of the CMIP5 models in all regions. Moreover, on average over individual regions, the projections from MRI-AGCM3.2 spread over roughly 0.8 of the uncertainty range from the high-performing CMIP5 models compared to 0.4 of the range of the full ensemble.

scholarly journals Precipitation in the Amazon and its relationship with moisture transport and tropical Pacific and Atlantic SST from the CMIP5 simulation

2015 ◽  
Vol 12 (1) ◽  
pp. 671-704 ◽  
Author(s):  
G. Martins ◽  
C. von Randow ◽  
G. Sampaio ◽  
A. J. Dolman
Keyword(s):  
Moisture Transport ◽  
General Circulation ◽  
Regional Analysis ◽  
Coupled Model ◽  
General Circulation Models ◽  
Moisture Convergence ◽  
Cmip5 Models ◽  
Wet Season ◽  
The Pacific ◽  
Eastern Edge

Abstract. Studies on numerical modeling in Amazonia show that the models fail to capture important aspects of climate variability in this region and it is important to understand the reasons that cause this drawback. Here, we study how the general circulation models of the Coupled Model Intercomparison Project Phase 5 (CMIP5) simulate the inter-relations between regional precipitation, moisture convergence and Sea Surface Temperature (SST) in the adjacent oceans, to assess how flaws in the representation of these processes can translate into biases in simulated rainfall in Amazonia. Using observational data (GPCP, CMAP, ERSST.v3, ERAI and evapotranspiration) and 21 numerical simulations from CMIP5 during the present climate (1979–2005) in June, July and August (JJA) and December, January and February (DJF), respectively, to represent dry and wet season characteristics, we evaluate how the models simulate precipitation, moisture transport and convergence, and pressure velocity (omega) in different regions of Amazonia. Thus, it is possible to identify areas of Amazonia that are more or less influenced by adjacent ocean SSTs. Our results showed that most of the CMIP5 models have poor skill in adequately representing the observed data. The regional analysis of the variables used showed that the underestimation in the dry season (JJA) was twice in relation to rainy season as quantified by the Standard Error of the Mean (SEM). It was found that Atlantic and Pacific SSTs modulate the northern sector of Amazonia during JJA, while in DJF Pacific SST only influences the eastern sector of the region. The analysis of moisture transport in JJA showed that moisture preferentially enters Amazonia via its eastern edge. In DJF this occurs both via its northern and eastern edge. The moisture balance is always positive, which indicates that Amazonia is a source of moisture to the atmosphere. Additionally, our results showed that during DJF the simulations in northeast sector of Amazonia have a strong bias in precipitation and an underestimation of moisture convergence due to the higher influence of biases in the Pacific SST. During JJA, a strong precipitation bias was observed in the southwest sector associated, also with a negative bias of moisture convergence, but with weaker influence of SSTs of adjacent oceans. The poor representation of precipitation-producing systems in Amazonia by the models and the difficulty of adequately representing the variability of SSTs in the Pacific and Atlantic oceans may be responsible for these underestimates in Amazonia.

scholarly journals Bias-corrected climate projections for South Asia from Coupled Model Intercomparison Project-6

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Vimal Mishra ◽  
Udit Bhatia ◽  
Amar Deep Tiwari
Keyword(s):  
Climate Change ◽  
Impact Assessment ◽  
South Asia ◽  
General Circulation ◽  
Coupled Model ◽  
General Circulation Models ◽  
River Basins ◽  
Climate Projections ◽  
Model Intercomparison ◽  
Intercomparison Project

Abstract Climate change is likely to pose enormous challenges for agriculture, water resources, infrastructure, and livelihood of millions of people living in South Asia. Here, we develop daily bias-corrected data of precipitation, maximum and minimum temperatures at 0.25° spatial resolution for South Asia (India, Pakistan, Bangladesh, Nepal, Bhutan, and Sri Lanka) and 18 river basins located in the Indian sub-continent. The bias-corrected dataset is developed using Empirical Quantile Mapping (EQM) for the historic (1951–2014) and projected (2015–2100) climate for the four scenarios (SSP126, SSP245, SSP370, SSP585) using output from 13 General Circulation Models (GCMs) from Coupled Model Intercomparison Project-6 (CMIP6). The bias-corrected dataset was evaluated against the observations for both mean and extremes of precipitation, maximum and minimum temperatures. Bias corrected projections from 13 CMIP6-GCMs project a warmer (3–5°C) and wetter (13–30%) climate in South Asia in the 21st century. The bias-corrected projections from CMIP6-GCMs can be used for climate change impact assessment in South Asia and hydrologic impact assessment in the sub-continental river basins.

scholarly journals The Caribbean Low-Level Jet and Its Relationship with Precipitation in IPCC AR4 Models

2011 ◽  
Vol 24 (22) ◽  
pp. 5935-5950 ◽  
Author(s):  
Elinor R. Martin ◽  
Courtney Schumacher
Keyword(s):  
General Circulation ◽  
Coupled Model ◽  
General Circulation Models ◽  
Coupled Models ◽  
Intergovernmental Panel ◽  
Low Level ◽  
The North ◽  
Moisture Fluxes ◽  
Low Level Jet ◽  
The Caribbean

Abstract A census of 19 coupled and 12 uncoupled model runs from the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) shows that all models have the ability to simulate the location and height of the Caribbean low-level jet (CLLJ); however, the observed semiannual cycle of the CLLJ magnitude was a challenge for the models to reproduce. In particular, model means failed to capture the strong July CLLJ peak as a result of the lack of westward and southward expansion of the North Atlantic subtropical high (NASH) between May and July. The NASH was also found to be too strong, particularly during the first 6 months of the year in the coupled model runs, which led to increased meridional sea level pressure gradients across the southern Caribbean and, hence, an overly strong CLLJ. The ability of the models to simulate the correlation between the CLLJ and regional precipitation varied based on season and region. During summer months, the negative correlation between the CLLJ and Caribbean precipitation anomalies was reproduced in the majority of models, with uncoupled models outperforming coupled models. The positive correlation between the CLLJ and the central U.S. precipitation during February was more challenging for the models, with the uncoupled models failing to reproduce a significant relationship. This may be a result of overactive convective parameterizations raining out too much moisture in the Caribbean meaning less is available for transport northward, or due to incorrect moisture fluxes over the Gulf of Mexico. The representation of the CLLJ in general circulation models has important consequences for accurate predictions and projections of future climate in the Caribbean and surrounding regions.

Assessing the spatio-temporal uncertainties in future meteorological droughts from CMIP5 models, emission scenarios and bias corrections

2020 ◽  
pp. 1-58
Author(s):  
Chuanhao Wu ◽  
Pat J.-F. Yeh ◽  
Jiali Ju ◽  
Yi-Ying Chen ◽  
Kai Xu ◽  
...  
Keyword(s):  
General Circulation ◽  
Standardized Precipitation Index ◽  
Coupled Model ◽  
General Circulation Models ◽  
Cmip5 Models ◽  
Northern Asia ◽  
Drought Characteristics ◽  
Uncertainty Sources ◽  
Spatio Temporal ◽  
The Standardized Precipitation Index

AbstractDrought projections are accompanied with large uncertainties due to varying estimates of future warming scenarios from different modelling and forcing data. Using the Standardized Precipitation Index (SPI), this study presents a global assessment of uncertainties in drought characteristics (severity S and frequency Df) projections based on the simulations of 28 general circulation models (GCMs) from the fifth phase of the Coupled Model Intercomparison Project (CMIP5). A hierarchical framework incorporating a variance–based global sensitivity analysis was developed to quantify the uncertainties in drought characteristics projections at various spatial (global and regional) and temporal (decadal and 30-yr) scales due to 28 GCMs, 3 Representative Concentration Pathway scenarios (RCP2.6, RCP4.5, RCP8.5), and 2 bias-correction (BC) methods. The results indicated that the largest uncertainty contribution in the globally projected S and Df is from the GCM (>60%), followed by BC (<35%) and RCP (<16%). Spatially, BC reduces the spreads among GCMs particularly in Northern Hemisphere (NH), leading to smaller GCM uncertainty in NH than Southern Hemisphere (SH). In contrast, the BC and RCP uncertainties are larger in NH than SH, and the BC uncertainty can be larger than GCM uncertainty for some regions (e.g., southwest Asia). At the decadal and 30-yr timescales, the contributions for 3 uncertainty sources show larger variability in S than Df projections, especially in SH. The GCM and BC uncertainties show overall decreasing trends with time, while the RCP uncertainty is expected to increase over time and even can be larger than BC uncertainty for some regions (e.g., northern Asia) by the end of this century.

Twenty-First-Century Snowfall and Snowpack Changes over the Southern California Mountains

2015 ◽  
Vol 29 (1) ◽  
pp. 91-110 ◽  
Author(s):  
Fengpeng Sun ◽  
Alex Hall ◽  
Marla Schwartz ◽  
Daniel B. Walton ◽  
Neil Berg
Keyword(s):  
General Circulation ◽  
Southern California ◽  
Snow Water Equivalent ◽  
Coupled Model ◽  
General Circulation Models ◽  
First Century ◽  
Climate Projections ◽  
Rcp Scenarios ◽  
Ensemble Mean ◽  
Twenty First Century

Abstract Future snowfall and snowpack changes over the mountains of Southern California are projected using a new hybrid dynamical–statistical framework. Output from all general circulation models (GCMs) in phase 5 of the Coupled Model Intercomparison Project archive is downscaled to 2-km resolution over the region. Variables pertaining to snow are analyzed for the middle (2041–60) and end (2081–2100) of the twenty-first century under two representative concentration pathway (RCP) scenarios: RCP8.5 (business as usual) and RCP2.6 (mitigation). These four sets of projections are compared with a baseline reconstruction of climate from 1981 to 2000. For both future time slices and scenarios, ensemble-mean total winter snowfall loss is widespread. By the mid-twenty-first century under RCP8.5, ensemble-mean winter snowfall is about 70% of baseline, whereas the corresponding value for RCP2.6 is somewhat higher (about 80% of baseline). By the end of the century, however, the two scenarios diverge significantly. Under RCP8.5, snowfall sees a dramatic further decline; 2081–2100 totals are only about half of baseline totals. Under RCP2.6, only a negligible further reduction from midcentury snowfall totals is seen. Because of the spread in the GCM climate projections, these figures are all associated with large intermodel uncertainty. Snowpack on the ground, as represented by 1 April snow water equivalent is also assessed. Because of enhanced snowmelt, the loss seen in snowpack is generally 50% greater than that seen in winter snowfall. By midcentury under RCP8.5, warming-accelerated spring snowmelt leads to snow-free dates that are about 1–3 weeks earlier than in the baseline period.

scholarly journals The Climatology of the Atmospheric Boundary Layer in Contemporary Global Climate Models

2018 ◽  
Vol 31 (22) ◽  
pp. 9151-9173 ◽  
Author(s):  
Richard Davy
Keyword(s):  
Boundary Layer ◽  
General Circulation ◽  
Climate Models ◽  
Global Climate ◽  
Coupled Model ◽  
General Circulation Models ◽  
Global Climate Models ◽  
Cmip5 Models ◽  
Seasonal Cycles ◽  
Layer Depth

Here, we present the climatology of the planetary boundary layer depth in 18 contemporary general circulation models (GCMs) in simulations of the late-twentieth-century climate that were part of phase 5 of the Coupled Model Intercomparison Project (CMIP5). We used a bulk Richardson methodology to establish the boundary layer depth from the 6-hourly synoptic-snapshot data available in the CMIP5 archives. We present an ensemble analysis of the climatological mean, diurnal cycle, and seasonal cycle of the boundary layer depth in these models and compare it to the climatologies from the ECMWF ERA-Interim reanalysis. Overall, we find that the CMIP5 models do a reasonably good job of reproducing the distribution of mean boundary layer depth, although the geographical patterns vary considerably between models. However, the models are biased toward weaker diurnal and seasonal cycles in the boundary layer depth and generally produce much deeper boundary layers at night and during the winter than are found in the reanalysis. These biases are likely to reduce the ability of these models to accurately represent other properties of the diurnal and seasonal cycles, and the sensitivity of these cycles to climate change.

scholarly journals Investigating Future Changes in Southern China Precipitation Characteristics Based on Dynamically Downscaled CMIP5 Climate Projections

2021 ◽  
Author(s):  
Ying Lung Liu ◽  
Chi-yung Tam ◽  
Hang Wai Tong ◽  
Kevin Cheung ◽  
Zhongfeng Xu
Keyword(s):  
General Circulation ◽  
Climate Model ◽  
Hydrological Cycle ◽  
Southern China ◽  
Coupled Model ◽  
General Circulation Models ◽  
Horizontal Resolution ◽  
Annual Number ◽  
Climate Projections ◽  
Budget Analysis

Abstract The Regional Climate Model version 4 (RegCM4) has been used to dynamically downscale outputs from four different general circulation models (GCM) participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) to the horizontal resolution of 25 km × 25km, in order to study 2050-to-2099 changes in the Southern China hydrological cycle according to Representative Concentration Pathway (RCP) 8.5, relative to the period of 1979 to 2003. The mean summertime precipitation is projected to increase by 0.5 – 1.5 mm/day over coastal Southern China, and with significantly enhanced interannual variability. In boreal spring, similar increase in both the seasonal mean and its year-to-year variation north of 25°N is also found. A novel moisture budget analysis shows that changes in mean background humidity (anomalous wind convergence) dominates the increase in the interannual precipitation variability in spring (summer). Extreme daily precipitation (based on the 95 th percentile) is projected to become more intense, roughly following the Clausius–Clapeyron relation for the aforementioned seasons. On the other hand, autumn mean rainfall rate will be reduced over a broad area in Southern China (although this might be subjected to models’ ability in capturing tropical cyclone activities). The annual number of maximum consecutive dry days (CDD) is found to increase by about 3 to 5 days over locations south of 32°N. Analyses of GCM raw outputs indicate that strengthened northerlies over coastal East Asia , which is likely associated with the so-called tropical expansion, are responsible for the drier autumn.

Arctic amplification as a rapid response to increased CO2

2021 ◽  
Author(s):  
Tyler Janoski ◽  
Michael Previdi ◽  
Gabriel Chiodo ◽  
Karen Smith ◽  
Lorenzo Polvani
Keyword(s):  
Sea Ice ◽  
General Circulation ◽  
Coupled Model ◽  
General Circulation Models ◽  
Forced Response ◽  
Arctic Sea Ice ◽  
Lapse Rate ◽  
The Arctic ◽  
Cmip5 Models ◽  
Arctic Amplification

&lt;p&gt;Arctic amplification (AA), or enhanced surface warming of the Arctic, is ubiquitous in observations, and in model simulations subjected to increased greenhouse gas (GHG) forcing. Despite its importance, the mechanisms driving AA are not entirely understood. Here, we show that in CMIP5 (Coupled Model Intercomparison Project 5) general circulation models (GCMs), AA develops within a few months following an instantaneous quadrupling of atmospheric CO&lt;sub&gt;2&lt;/sub&gt;. We find that this rapid AA response can be attributed to the lapse rate feedback, which acts to disproportionately warm the Arctic, even before any significant changes in Arctic sea ice occur. Only on longer timescales (beyond the first few months) does the decrease in sea ice become an important contributor to AA via the albedo feedback and increased ocean-to-atmosphere heat flux. An important limitation of our CMIP5 analysis is that internal climate variability is large on the short time scales considered. To overcome this limitation &amp;#8211; and thus better isolate the GHG-forced response &amp;#8211; we produced a large ensemble (100 members) of instantaneous CO&lt;sub&gt;2&lt;/sub&gt;-quadrupling simulations using a single GCM, the NCAR Community Earth System Model (CESM1). In our new CESM1 ensemble we find the same rapid AA response seen in the CMIP5 models, confirming that AA ultimately owes its existence to fast atmospheric processes.&lt;/p&gt;

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