scholarly journals An Intercomparison of Observational, Reanalysis, Satellite, and Coupled Model Data on Mean Rainfall in the Caribbean

2009 ◽  
Vol 10 (2) ◽  
pp. 413-430 ◽  
Author(s):  
Mark R. Jury
Keyword(s):  
General Circulation ◽  
Weather Prediction ◽  
Coupled Model ◽  
General Circulation Models ◽  
Passive Microwave ◽  
Annual Rainfall ◽  
Data Set ◽  
Rain Gauges ◽  
The Caribbean ◽  
Key Features

Abstract This study examines the spatial variability of mean annual rainfall in the Caribbean in the satellite era 1979–2000. Intercomparisons of gridded rainfall fields from conventional stations, satellite estimators, reanalysis products, and coupled general circulation models (CGCMs) are made, with a focus on the Antilles island chain and their land–sea transitions. The rainfall products are rated for their ability to capture a number of key features, including (i) topographically enhanced precipitation over the larger western Antilles islands of Cuba, Jamaica, Hispanola, and Puerto Rico; (ii) the rain shadow west of Hispanola; (iii) the two dry zones where SSTs are low: north of Venezuela and north of the Lesser Antilles; and (iv) the wet axis extending north of Trinidad. The various monitoring and modeling systems produce gridded rainfall fields at resolutions from 50 to 280 km, from station reconstructions, satellite estimates, blended and reanalysis products, and CGCM climatologies with respect to surface forcing fields. Wet and dry biases were found in many of the reanalysis and satellite products, respectively—either over the whole Caribbean or in a certain sector. The intercomparison found some measure of consensus, but no single product is without discrepancy. High-resolution passive microwave satellite rainfall estimates [Climate Prediction Center’s multisaltellite passive microwave, IR morphed product (cMOR)] appear “most representative”; however, the climatology is short (2003–07) and the field is generally drier than the consensus. Of the conventional products, decadal variability of climate interpolated rain gauges (DEKL), World Climate Research Programme’s (WCRP) blended rain gauges, the Comprehensive Ocean–Atmosphere Data Set (COADS), and an operational climate anomaly monitoring system of NCEP (CAMS) perform well. Among the satellite estimators, the Global Precipitation Climatology Project’s blended gauge and IR satellite (GPCP) and outgoing longwave radiation (OLR) capture the key features and ocean–island transitions. The Center for Ocean–Land–Atmosphere Studies [COLA; the coupled model, part of the Coupled Model Intercomparison Project (CMIP, phase 3)] and the climate forecast system of the NCEP (CFS) models perform reasonably, but NCAR’s Parallel Climate Model (PCM; the CGCM’s historical run of CMIP3) fares poorly. The version 2 hindcast of the operational Medium-Range Forecast (MRF) weather prediction model (REAN) captures the smaller wet zones and topographically enhanced features, but it does not handle the broad oceanic dry zones well, as the input from the operational climate data assimilation system of NCEP (CDAS) has a wet bias. Of the various key rainfall features, high rainfall over southern Cuba and the rain shadow west of Hispanola are poorly handled by most products. The wet axis north of Trinidad and the dry zone north of Venezuela are well represented in many climatologies.

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.

scholarly journals Evaluation of Bayesian Multimodel Estimation in Surface Incident Shortwave Radiation Simulation over High Latitude Areas

2019 ◽  
Vol 11 (15) ◽  
pp. 1776 ◽  
Author(s):  
Weiyu Zhang ◽  
Xiaotong Zhang ◽  
Wenhong Li ◽  
Ning Hou ◽  
Yu Wei ◽  
...  
Keyword(s):  
High Latitude ◽  
General Circulation ◽  
Bayesian Model Averaging ◽  
Radiant Energy ◽  
Coupled Model ◽  
Model Averaging ◽  
Energy System ◽  
General Circulation Models ◽  
Shortwave Radiation ◽  
Data Set

Surface incident shortwave radiation (SSR) is crucial for understanding the Earth’s climate change issues. Simulations from general circulation models (GCMs) are one of the most practical ways to produce long-term global SSR products. Although previous studies have comprehensively assessed the performance of the GCMs in simulating SSR globally or regionally, studies assessing the performance of these models over high-latitude areas are sparse. This study evaluated and intercompared the SSR simulations of 48 GCMs participating in the fifth phase of the Coupled Model Intercomparison Project (CMIP5) using quality-controlled SSR surface measurements at 44 radiation sites from three observation networks (GC-NET, BSRN, and GEBA) and the SSR retrievals from the Clouds and the Earth’s Radiant Energy System, Energy Balanced and Filled (CERES EBAF) data set over high-latitude areas from 2000 to 2005. Furthermore, this study evaluated the performance of the SSR estimations of two multimodel ensemble methods, i.e., the simple model averaging (SMA) and the Bayesian model averaging (BMA) methods. The seasonal performance of the SSR estimations of individual GCMs, the SMA method, and the BMA method were also intercompared. The evaluation results indicated that there were large deficiencies in the performance of the individual GCMs in simulating SSR, and these GCM SSR simulations did not show a tendency to overestimate the SSR over high-latitude areas. Moreover, the ensemble SSR estimations generated by the SMA and BMA methods were superior to all individual GCM SSR simulations over high-latitude areas, and the estimations of the BMA method were the best compared to individual GCM simulations and the SMA method-based estimations. Compared to the CERES EBAF SSR retrievals, the uncertainties of the SSR estimations of the GCMs, the SMA method, and the BMA method are relatively large during summer.

scholarly journals Evaluation of General Circulation Models over the Upper Ouémé River Basin in the Republic of Benin

Hydrology ◽  
2020 ◽  
Vol 7 (1) ◽  
pp. 11 ◽  
Author(s):  
André Attogouinon ◽  
Agnidé E. Lawin ◽  
Jean-François Deliège
Keyword(s):  
Climate Change ◽  
River Basin ◽  
General Circulation ◽  
Climate Model ◽  
Coupled Model ◽  
Daily Rainfall ◽  
Extreme Rainfall ◽  
General Circulation Models ◽  
Future Climate Change ◽  
Data Set

This study assessed the performance of eight general circulation models (GCMs) implemented in the upper Ouémé River basin in Benin Republic (West Africa) during the Fifth Assessment Report on Climate Change. Historical rainfall simulations of the climate model of Rossby Regional Centre (RCA4) driven by eight Coupled Model Intercomparison Project (CMIP5) GCMs over a 55-year period (1951 to 2005) are evaluated using the observational data set. Apart from daily rainfall, other rainfall parameters calculated from observed and simulated rainfall were compared. U-test and other statistical criteria (R2, MBE, MAE, RMSE and standard of standard deviations) were used. According to the results, the simulations correctly reproduce the interannual variability of precipitation in the upper Ouémé River basin. However, the models tend to produce drizzle. Especially, the overestimation of April, May and November rains not only explains the overestimation of seasonal and annual cumulative rainfall but also the early onset of the rainy season and its late withdrawal. However, we noted that this overestimation magnitude varies from one model to another. As for extreme rainfall indices, the models reproduced them poorly. The CanESM2, CNRM-CM5 and EC-EARTH models perform well for daily rainfall. A trade-off is formulated to select the common MPI-ESM-LR, GFDL-ESM2M, NorESM1-M and CanESM2 models for different rainfall parameters for the reliable projection of rainfall in the area. However, the MPI-ESM-LR model is a valuable tool for studying future climate change.

scholarly journals The MJO in a Coarse-Resolution GCM with a Stochastic Multicloud Parameterization

2015 ◽  
Vol 72 (1) ◽  
pp. 55-74 ◽  
Author(s):  
Qiang Deng ◽  
Boualem Khouider ◽  
Andrew J. Majda
Keyword(s):  
General Circulation ◽  
Large Scale ◽  
Deterministic Model ◽  
Weather Prediction ◽  
General Circulation Models ◽  
Radar Data ◽  
Equation Model ◽  
Death Process ◽  
Relevant Parameter ◽  
Convective Systems

Abstract The representation of the Madden–Julian oscillation (MJO) is still a challenge for numerical weather prediction and general circulation models (GCMs) because of the inadequate treatment of convection and the associated interactions across scales by the underlying cumulus parameterizations. One new promising direction is the use of the stochastic multicloud model (SMCM) that has been designed specifically to capture the missing variability due to unresolved processes of convection and their impact on the large-scale flow. The SMCM specifically models the area fractions of the three cloud types (congestus, deep, and stratiform) that characterize organized convective systems on all scales. The SMCM captures the stochastic behavior of these three cloud types via a judiciously constructed Markov birth–death process using a particle interacting lattice model. The SMCM has been successfully applied for convectively coupled waves in a simplified primitive equation model and validated against radar data of tropical precipitation. In this work, the authors use for the first time the SMCM in a GCM. The authors build on previous work of coupling the High-Order Methods Modeling Environment (HOMME) NCAR GCM to a simple multicloud model. The authors tested the new SMCM-HOMME model in the parameter regime considered previously and found that the stochastic model drastically improves the results of the deterministic model. Clear MJO-like structures with many realistic features from nature are reproduced by SMCM-HOMME in the physically relevant parameter regime including wave trains of MJOs that organize intermittently in time. Also one of the caveats of the deterministic simulation of requiring a doubling of the moisture background is not required anymore.

scholarly journals Cloud and Precipitation Parameterizations in Modeling and Variational Data Assimilation: A Review

2007 ◽  
Vol 64 (11) ◽  
pp. 3766-3784 ◽  
Author(s):  
Philippe Lopez
Keyword(s):  
Data Assimilation ◽  
General Circulation ◽  
Large Scale ◽  
Weather Prediction ◽  
General Circulation Models ◽  
Variational Data Assimilation ◽  
Current Status ◽  
General Question ◽  
Observation Error ◽  
Moist Processes

Abstract This paper first reviews the current status, issues, and limitations of the parameterizations of atmospheric large-scale and convective moist processes that are used in numerical weather prediction and climate general circulation models. Both large-scale (resolved) and convective (subgrid scale) moist processes are dealt with. Then, the general question of the inclusion of diabatic processes in variational data assimilation systems is addressed. The focus is put on linearity and resolution issues, the specification of model and observation error statistics, the formulation of the control vector, and the problems specific to the assimilation of observations directly affected by clouds and precipitation.

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).

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 PLASIM-ENTSem: a spatio-temporal emulator of future climate change for impacts assessment

2013 ◽  
Vol 6 (2) ◽  
pp. 3349-3380 ◽  
Author(s):  
P. B. Holden ◽  
N. R. Edwards ◽  
P. H. Garthwaite ◽  
K. Fraedrich ◽  
F. Lunkeit ◽  
...  
Keyword(s):  
Radiative Forcing ◽  
General Circulation ◽  
Coupled Model ◽  
Co2 Concentration ◽  
General Circulation Models ◽  
Climate Impacts ◽  
Future Climate ◽  
Future Climate Change ◽  
Climate Data ◽  
Spatio Temporal

Abstract. Many applications in the evaluation of climate impacts and environmental policy require detailed spatio-temporal projections of future climate. To capture feedbacks from impacted natural or socio-economic systems requires interactive two-way coupling but this is generally computationally infeasible with even moderately complex general circulation models (GCMs). Dimension reduction using emulation is one solution to this problem, demonstrated here with the GCM PLASIM-ENTS. Our approach generates temporally evolving spatial patterns of climate variables, considering multiple modes of variability in order to capture non-linear feedbacks. The emulator provides a 188-member ensemble of decadally and spatially resolved (~ 5° resolution) seasonal climate data in response to an arbitrary future CO2 concentration and radiative forcing scenario. We present the PLASIM-ENTS coupled model, the construction of its emulator from an ensemble of transient future simulations, an application of the emulator methodology to produce heating and cooling degree-day projections, and the validation of the results against empirical data and higher-complexity models. We also demonstrate the application to estimates of sea-level rise and associated uncertainty.

Evaluation of the tail of the probability distribution of daily and sub-daily precipitation in CMIP6 models

2021 ◽  
pp. 1-61
Author(s):  
Jesse Norris ◽  
Alex Hall ◽  
J. David Neelin ◽  
Chad W. Thackeray ◽  
Di Chen
Keyword(s):  
General Circulation ◽  
Daily Precipitation ◽  
Precipitation Amount ◽  
Coupled Model ◽  
Lower Troposphere ◽  
General Circulation Models ◽  
Intercomparison Project ◽  
Cloud Tops ◽  
The Tropics ◽  
Fraction Models

AbstractDaily and sub-daily precipitation extremes in historical Coupled-Model-Intercomparison-Project-Phase-6 (CMIP6) simulations are evaluated against satellite-based observational estimates. Extremes are defined as the precipitation amount exceeded every x years, ranging from 0.01–10, encompassing the rarest events that are detectable in the observational record without noisy results. With increasing temporal resolution there is an increased discrepancy between models and observations: for daily extremes the multi-model median underestimates the highest percentiles by about a third, and for 3-hourly extremes by about 75% in the tropics. The novelty of the current study is that, to understand the model spread, we evaluate the 3-D structure of the atmosphere when extremes occur. In midlatitudes, where extremes are simulated predominantly explicitly, the intuitive relationship exists whereby higher-resolution models produce larger extremes (r=–0.49), via greater vertical velocity. In the tropics, the convective fraction (the fraction of precipitation simulated directly from the convective scheme) is more relevant. For models below 60% convective fraction, precipitation amount decreases with convective fraction (r=–0.63), but above 75% convective fraction, this relationship breaks down. In the lower-convective-fraction models, there is more moisture in the lower troposphere, closer to saturation. In the higher-convective-fraction models, there is deeper convection and higher cloud tops, which appears to be more physical. Thus, the low-convective models are mostly closer to the observations of extreme precipitation in the tropics, but likely for the wrong reasons. These inter-model differences in the environment in which extremes are simulated hold clues into how parameterizations could be modified in general circulation models to produce more credible 21st-Century projections.

scholarly journals Performance Assessment of General Circulation Model in Simulating Daily Precipitation and Temperature Using Multiple Gridded Datasets

Water ◽  
2018 ◽  
Vol 10 (12) ◽  
pp. 1793 ◽  
Author(s):  
Najeebullah Khan ◽  
Shamsuddin Shahid ◽  
Kamal Ahmed ◽  
Tarmizi Ismail ◽  
Nadeem Nawaz ◽  
...  
Keyword(s):  
General Circulation ◽  
Coupled Model ◽  
Circulation Model ◽  
General Circulation Models ◽  
Daily Maximum ◽  
Gridded Data ◽  
Max Planck ◽  
Meteorological Forcing ◽  
Temperature And Precipitation ◽  
Feature Selection Approach

The performance of general circulation models (GCMs) in a region are generally assessed according to their capability to simulate historical temperature and precipitation of the region. The performance of 31 GCMs of the Coupled Model Intercomparison Project Phase 5 (CMIP5) is evaluated in this study to identify a suitable ensemble for daily maximum, minimum temperature and precipitation for Pakistan using multiple sets of gridded data, namely: Asian Precipitation–Highly-Resolved Observational Data Integration Towards Evaluation (APHRODITE), Berkeley Earth Surface Temperature (BEST), Princeton Global Meteorological Forcing (PGF) and Climate Prediction Centre (CPC) data. An entropy-based robust feature selection approach known as symmetrical uncertainty (SU) is used for the ranking of GCM. It is known from the results of this study that the spatial distribution of best-ranked GCMs varies for different sets of gridded data. The performance of GCMs is also found to vary for both temperatures and precipitation. The Commonwealth Scientific and Industrial Research Organization, Australia (CSIRO)-Mk3-6-0 and Max Planck Institute (MPI)-ESM-LR perform well for temperature while EC-Earth and MIROC5 perform well for precipitation. A trade-off is formulated to select the common GCMs for different climatic variables and gridded data sets, which identify six GCMs, namely: ACCESS1-3, CESM1-BGC, CMCC-CM, HadGEM2-CC, HadGEM2-ES and MIROC5 for the reliable projection of temperature and precipitation of Pakistan.

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