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.

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.

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 Projection of Water Availability and Sustainability in Nigeria Due to Climate Change

2021 ◽  
Vol 13 (11) ◽  
pp. 6284
Author(s):  
Mohammed Sanusi Shiru ◽  
Shamsuddin Shahid ◽  
Inhwan Park
Keyword(s):  
Climate Change ◽  
Water Availability ◽  
General Circulation ◽  
Water Storage ◽  
Coupled Model ◽  
General Circulation Models ◽  
Research Unit ◽  
Wet Season ◽  
Spatiotemporal Changes ◽  
Terrestrial Water

This study projects water availability and sustainability in Nigeria due to climate change. This study used Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage data (TWS), Global Precipitation Climatology Center (GPCC) precipitation data and Climate Research Unit (CRU) temperature data. Four general circulation models (GCMs) of the Coupled Model Intercomparison Project 5 were downscaled using the best of four downscaling methods. Two machine learning (ML) models, RF and SVM, were developed to simulate GRACE TWS data for the period 2002–2016 and were then used for the projection of spatiotemporal changes in TWS. The projected TWS data were used to assess the spatiotemporal changes in water availability and sustainability based on the reliability–resiliency–vulnerability (RRV) concept. This study revealed that linear scaling was the best for downscaling over Nigeria. RF had better performance than SVM in modeling TWS for the study area. This study also revealed there would be decreases in water storage during the wet season (June–September) and increases in the dry season (January–May). Decreases in projected water availability were in the range of 0–12 mm for the periods 2010–2039, 2040–2069, and 2070–2099 under RCP2.6 and in the range of 0–17 mm under RCP8.5 during the wet season. Spatially, annual changes in water storage are expected to increase in the northern part and decrease in the south, particularly in the country’s southeast. Groundwater sustainability was higher during the period 2070–2099 under all RCPs compared to the other periods and this can be attributed to the expected increases in rainfall during this period.

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;

scholarly journals Ranking of CMIP5 GCM Skills in Simulating Observed Precipitation over the Lower Mekong Basin, Using an Improved Score-Based Method

Water ◽  
2018 ◽  
Vol 10 (12) ◽  
pp. 1868 ◽  
Author(s):  
Yunfeng Ruan ◽  
Zhijun Yao ◽  
Rui Wang ◽  
Zhaofei Liu
Keyword(s):  
General Circulation ◽  
Coupled Model ◽  
General Circulation Models ◽  
Dry Season ◽  
Positive Trend ◽  
Wet Season ◽  
Mekong Basin ◽  
Good Ability ◽  
Lower Mekong Basin ◽  
Annual Time

This study assessed the performances of 34 Coupled Model Intercomparison Project Phase 5 (CMIP5) general circulation models (GCMs) in reproducing observed precipitation over the Lower Mekong Basin (LMB). Observations from gauge-based data of the Asian Precipitation-Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE) precipitation data were obtained from 1975 to 2004. An improved score-based method was used to rank the performance of the GCMs in reproducing the observed precipitation over the LMB. The results revealed that most GCMs effectively reproduced precipitation patterns for the mean annual cycle, but they generally overestimated the observed precipitation. The GCMs showed good ability in reproducing the time series characteristics of precipitation for the annual period compared to those for the wet and dry seasons. Meanwhile, the GCMs obviously reproduced the spatial characteristics of precipitation for the dry season better than those for annual time and the wet season. More than 50% of the GCMs failed to reproduce the positive trend of the observed precipitation for the wet season and the dry season (approximately 52.9% and 64.7%, respectively), and approximately 44.1% of the GCMs failed to reproduce positive trend for annual time over the LMB. Furthermore, it was also revealed that there existed different robust criteria for assessing the GCMs’ performances at a seasonal scale, and using multiple criteria was superior to a single criterion in assessing the GCMs’ performances. Overall, the better-performed GCMs were obtained, which can provide useful information for future precipitation projection and policy-making over the LMB.

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 Precipitation and Moisture in Four Leading CMIP5 Models: Biases across Large-Scale Circulation Regimes and Their Attribution to Dynamic and Thermodynamic Factors

2018 ◽  
Vol 31 (13) ◽  
pp. 5089-5106 ◽  
Author(s):  
Mengmiao Yang ◽  
Guang J. Zhang ◽  
De-Zheng Sun
Keyword(s):  
General Circulation ◽  
Large Scale ◽  
Coupled Model ◽  
General Circulation Models ◽  
Cmip5 Models ◽  
Frequency Of Occurrence ◽  
Upward Motion ◽  
Large Scale Circulation ◽  
Intercomparison Project ◽  
Strong Upward Motion

As key variables in general circulation models, precipitation and moisture in four leading models from CMIP5 (phase 5 of the Coupled Model Intercomparison Project) are analyzed, with a focus on four tropical oceanic regions. It is found that precipitation in these models is overestimated in most areas. However, moisture bias has large intermodel differences. The model biases in precipitation and moisture are further examined in conjunction with large-scale circulation by regime-sorting analysis. Results show that all models consistently overestimate the frequency of occurrence of strong upward motion regimes and peak descending regimes of 500-hPa vertical velocity [Formula: see text]. In a given [Formula: see text] regime, models produce too much precipitation compared to observation and reanalysis. But for moisture, their biases differ from model to model and also from level to level. Furthermore, error causes are revealed through decomposing contribution biases into dynamic and thermodynamic components. For precipitation, the contribution errors in strong upward motion regimes are attributed to the overly frequent [Formula: see text]. In the weak upward motion regime, the biases in the dependence of precipitation on [Formula: see text] and the [Formula: see text] probability density function (PDF) make comparable contributions, but often of opposite signs. On the other hand, the biases in column-integrated water vapor contribution are mainly due to errors in the frequency of occurrence of [Formula: see text], while thermodynamic components contribute little. These findings suggest that errors in the frequency of [Formula: see text] occurrence are a significant cause of biases in the precipitation and moisture simulation.

scholarly journals Madden–Julian Oscillation Pacific Teleconnections: The Impact of the Basic State and MJO Representation in General Circulation Models

2017 ◽  
Vol 30 (12) ◽  
pp. 4567-4587 ◽  
Author(s):  
Stephanie A. Henderson ◽  
Eric D. Maloney ◽  
Seok-Woo Son
Keyword(s):  
General Circulation ◽  
Coupled Model ◽  
General Circulation Models ◽  
Teleconnection Pattern ◽  
Madden Julian Oscillation ◽  
Horizontal Structure ◽  
Teleconnection Patterns ◽  
Subtropical Jet ◽  
The Pacific ◽  
The Impact

Teleconnection patterns associated with the Madden–Julian oscillation (MJO) significantly alter extratropical circulations, impacting weather and climate phenomena such as blocking, monsoons, the North Atlantic Oscillation, and the Pacific–North American pattern. However, the MJO has been extremely difficult to simulate in many general circulation models (GCMs), and many GCMs contain large biases in the background flow, presenting challenges to the simulation of MJO teleconnection patterns and associated extratropical impacts. In this study, the database from phase 5 of the Coupled Model Intercomparison Project (CMIP5) is used to assess the impact of model MJO and basic state quality on MJO teleconnection pattern quality, and a simple dry linear baroclinic model is employed to understand the results. Even in GCMs assessed to have good MJOs, large biases in the MJO teleconnection patterns are produced as a result of errors in the zonal extent of the Pacific subtropical jet. The horizontal structure of Indo-Pacific MJO heating in good MJO models is found to have modest impacts on the teleconnection pattern skill, in agreement with previous studies that have demonstrated little sensitivity to the location of tropical heating near the subtropical jet. However, MJO heating east of the date line can alter the teleconnection pathways over North America. Results show that GCMs with poor basic states can have equally low skill in reproducing the MJO teleconnection patterns as GCMs with poor MJO quality, suggesting that both the basic state and the MJO must be well represented in order to reproduce the correct teleconnection patterns.

scholarly journals Evaluation of Surface Upward Longwave Radiation in the CMIP6 Models with Ground and Satellite Observations

2021 ◽  
Vol 13 (21) ◽  
pp. 4464
Author(s):  
Jiawen Xu ◽  
Xiaotong Zhang ◽  
Chunjie Feng ◽  
Shuyue Yang ◽  
Shikang Guan ◽  
...  
Keyword(s):  
General Circulation ◽  
Bayesian Model Averaging ◽  
Mean Squared Error ◽  
Radiant Energy ◽  
Coupled Model ◽  
Model Averaging ◽  
Longwave Radiation ◽  
Energy System ◽  
General Circulation Models ◽  
The Individual

Surface upward longwave radiation (SULR) is an indicator of thermal conditions over the Earth’s surface. In this study, we validated the simulated SULR from 51 Coupled Model Intercomparison Project (CMIP6) general circulation models (GCMs) through a comparison with ground measurements and satellite-retrieved SULR from the Clouds and the Earth’s Radiant Energy System, Energy Balanced and Filled (CERES EBAF). Moreover, we improved the SULR estimations by a fusion of multiple CMIP6 GCMs using multimodel ensemble (MME) methods. Large variations were found in the monthly mean SULR among the 51 CMIP6 GCMs; the bias and root mean squared error (RMSE) of the individual CMIP6 GCMs at 133 sites ranged from −3 to 24 W m−2 and 22 to 38 W m−2, respectively, which were higher than those found between the CERES EBAF and GCMs. The CMIP6 GCMs did not improve the overestimation of SULR compared to the CMIP5 GCMs. The Bayesian model averaging (BMA) method showed better performance in simulating SULR than the individual GCMs and simple model averaging (SMA) method, with a bias of 0 W m−2 and an RMSE of 19.29 W m−2 for the 133 sites. In terms of the global annual mean SULR, our best estimation for the CMIP6 GCMs using the BMA method was 392 W m−2 during 2000–2014. We found that the SULR varied between 386 and 393 W m−2 from 1850 to 2014, exhibiting an increasing tendency of 0.2 W m−2 per decade (p < 0.05).

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