scholarly journals Evaluation of the Performance of CMIP6 Models in Reproducing Rainfall Patterns over North Africa

Atmosphere ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 475
Author(s):  
Hassen Babaousmail ◽  
Rongtao Hou ◽  
Brian Ayugi ◽  
Moses Ojara ◽  
Hamida Ngoma ◽  
...  
Keyword(s):  
North Africa ◽  
Rainfall Variability ◽  
Coupled Model ◽  
Cumulative Distribution ◽  
Climatic Research Unit ◽  
Model Ensemble ◽  
Empirical Cumulative Distribution Function ◽  
Cycle Simulation ◽  
Taylor Diagram ◽  
Statistical Metrics

This study assesses the performance of historical rainfall data from the Coupled Model Intercomparison Project phase 6 (CMIP6) in reproducing the spatial and temporal rainfall variability over North Africa. Datasets from Climatic Research Unit (CRU) and Global Precipitation Climatology Centre (GPCC) are used as proxy to observational datasets to examine the capability of 15 CMIP6 models’ and their ensemble in simulating rainfall during 1951–2014. In addition, robust statistical metrics, empirical cumulative distribution function (ECDF), Taylor diagram (TD), and Taylor skill score (TSS) are utilized to assess models’ performance in reproducing annual and seasonal and monthly rainfall over the study domain. Results show that CMIP6 models satisfactorily reproduce mean annual climatology of dry/wet months. However, some models show a slight over/under estimation across dry/wet months. The models’ overall top ranking from all the performance analyses ranging from mean cycle simulation, trend analysis, inter-annual variability, ECDFs, and statistical metrics are as follows: EC-Earth3-Veg, UKESM1-0-LL, GFDL-CM4, NorESM2-LM, IPSL-CM6A-LR, and GFDL-ESM4. The mean model ensemble outperformed the individual CMIP6 models resulting in a TSS ratio (0.79). For future impact studies over the study domain, it is advisable to employ the multi-model ensemble of the best performing models.

scholarly journals Evaluation of Historical CMIP6 Model Simulations of Seasonal Mean Temperature over Pakistan during 1970–2014

Atmosphere ◽  
2020 ◽  
Vol 11 (9) ◽  
pp. 1005
Author(s):  
Rizwan Karim ◽  
Guirong Tan ◽  
Brian Ayugi ◽  
Hassen Babaousmail ◽  
Fei Liu
Keyword(s):  
Coupled Model ◽  
Research Unit ◽  
Cumulative Distribution ◽  
Climatic Research Unit ◽  
Mean Temperature ◽  
Scale Models ◽  
Intercomparison Project ◽  
June July ◽  
Cumulative Distribution Frequency ◽  
Distribution Frequency

This work employed recent model outputs from coupled model intercomparison project phase six to simulate surface mean temperature during the June–July–August (JJA) and December–January–February (DJF) seasons for 1970–2014 over Pakistan. The climatic research unit (CRU TS4.03) dataset was utilized as benchmark data to analyze models’ performance. The JJA season exhibited the highest mean temperature, whilst DJF displayed the lowest mean temperature in the whole study period. The JJA monthly empirical cumulative distribution frequency (ECDF) range (26 to 28 °C) was less than that of DJF (7 to 10 °C) since JJA matched closely to CRU. The JJA and DJF seasons are warming, with higher warming trends in winters than in summers. On temporal scale, models performed better in JJA with overall low bias, low RMSE (root mean square error), and higher positive CC (correlation coefficient) values. DJF performance was undermined with higher bias and RMSE with weak positive correlation estimates. Overall, CanESM5, CESM2, CESM2-WACCM, GFDL-CM4, HadGEM-GC31-LL, MPI-ESM1-2-LR, MPI-ESM1-2-HR, and MRI-ESM-0 performed better for JJA and DJF.

Multi-model ensemble projections of soil moisture drought over North Africa and the Sahel region under 1.5, 2, and 3 °C global warming

2021 ◽  
Vol 167 (3-4) ◽  
Author(s):  
Ahmed Elkouk ◽  
Zine El Abidine El Morjani ◽  
Yadu Pokhrel ◽  
Abdelghani Chehbouni ◽  
Abdelfattah Sifeddine ◽  
...  
Keyword(s):  
Global Warming ◽  
Soil Moisture ◽  
North Africa ◽  
Model Ensemble ◽  
Sahel Region ◽  
Ensemble Projections

scholarly journals Developing a common, flexible and efficient framework for weakly coupled ensemble data assimilation based on C-Coupler2.0

2021 ◽  
Vol 14 (5) ◽  
pp. 2635-2657
Author(s):  
Chao Sun ◽  
Li Liu ◽  
Ruizhe Li ◽  
Xinzhu Yu ◽  
Hao Yu ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Data Exchange ◽  
Coupled Model ◽  
Earth System ◽  
Model Ensemble ◽  
Coupled Models ◽  
Weakly Coupled ◽  
Abstract Data ◽  
Global Communications ◽  
Earth System Models

Abstract. Data assimilation (DA) provides initial states of model runs by combining observational information and models. Ensemble-based DA methods that depend on the ensemble run of a model have been widely used. In response to the development of seamless prediction based on coupled models or even Earth system models, coupled DA is now in the mainstream of DA development. In this paper, we focus on the technical challenges in developing a coupled ensemble DA system, especially how to conveniently achieve efficient interaction between the ensemble of the coupled model and the DA methods. We first propose a new DA framework, DAFCC1 (Data Assimilation Framework based on C-Coupler2.0, version 1), for weakly coupled ensemble DA, which enables users to conveniently integrate a DA method into a model as a procedure that can be directly called by the model ensemble. DAFCC1 automatically and efficiently handles data exchanges between the model ensemble members and the DA method without global communications and does not require users to develop extra code for implementing the data exchange functionality. Based on DAFCC1, we then develop an example weakly coupled ensemble DA system by combining an ensemble DA system and a regional atmosphere–ocean–wave coupled model. This example DA system and our evaluations demonstrate the correctness of DAFCC1 in developing a weakly coupled ensemble DA system and the effectiveness in accelerating an offline DA system that uses disk files as the interfaces for the data exchange functionality.

scholarly journals Optimization of Rain Gauge Networks for Arid Regions Based on Remote Sensing Data

2021 ◽  
Vol 13 (21) ◽  
pp. 4243
Author(s):  
Mona Morsy ◽  
Ruhollah Taghizadeh-Mehrjardi ◽  
Silas Michaelides ◽  
Thomas Scholten ◽  
Peter Dietrich ◽  
...  
Keyword(s):  
Arid Regions ◽  
Flash Flood ◽  
Primary Source ◽  
Remote Sensing Data ◽  
Test Site ◽  
Rain Gauge ◽  
Cumulative Distribution ◽  
Climatic Data ◽  
Empirical Cumulative Distribution Function ◽  
Rain Gauges

Water depletion is a growing problem in the world’s arid and semi-arid areas, where groundwater is the primary source of fresh water. Accurate climatic data must be obtained to protect municipal water budgets. Unfortunately, the majority of these arid regions have a sparsely distributed number of rain gauges, which reduces the reliability of the spatio-temporal fields generated. The current research proposes a series of measures to address the problem of data scarcity, in particular regarding in-situ measurements of precipitation. Once the issue of improving the network of ground precipitation measurements is settled, this may pave the way for much-needed hydrological research on topics such as the spatiotemporal distribution of precipitation, flash flood prevention, and soil erosion reduction. In this study, a k-means cluster analysis is used to determine new locations for the rain gauge network at the Eastern side of the Gulf of Suez in Sinai. The clustering procedure adopted is based on integrating a digital elevation model obtained from The Shuttle Radar Topography Mission (SRTM 90 × 90 m) and Integrated Multi-Satellite Retrievals for GPM (IMERG) for four rainy events. This procedure enabled the determination of the potential centroids for three different cluster sizes (3, 6, and 9). Subsequently, each number was tested using the Empirical Cumulative Distribution Function (ECDF) in an effort to determine the optimal one. However, all the tested centroids exhibited gaps in covering the whole range of elevations and precipitation of the test site. The nine centroids with the five existing rain gauges were used as a basis to calculate the error kriging. This procedure enabled decreasing the error by increasing the number of the proposed gauges. The resulting points were tested again by ECDF and this confirmed the optimum of thirty-one suggested additional gauges in covering the whole range of elevations and precipitation records at the study site.

The role of air-sea coupling on the simulation of intraseasonal rainfall variability over the South Pacific in ECHAM5-SIT

2021 ◽  
Author(s):  
Sunil Kumar Pariyar ◽  
Noel Keenlyside ◽  
Wan-Ling Tseng
Keyword(s):  
General Circulation ◽  
Rainfall Variability ◽  
Intraseasonal Variability ◽  
Phase Relationship ◽  
Coupled Model ◽  
South Pacific ◽  
South Pacific Convergence Zone ◽  
The South ◽  
The Impact

<p><span>We investigate the impact of air-sea coupling on the simulation of the intraseasonal variability of rainfall over the South Pacific using the ECHAM5 atmospheric general circulation model coupled with Snow-Ice-Thermocline (SIT) ocean model. We compare the fully coupled simulation with two uncoupled simulations forced with sea surface temperature (SST) climatology and daily SST from the coupled model. The intraseasonal rainfall variability over the South Pacific Convergence Zone (SPCZ) is reduced by 17% in the uncoupled model forced with SST climatology and increased by 8% in the uncoupled simulation forced with daily SST. The coupled model best simulates the key characteristics of the two intraseasonal rainfall modes of variability in the South Pacific, as identified by an Empirical Orthogonal Function (EOF) analysis. The spatial structure of the two EOF modes in all three simulations is very similar, suggesting these modes are independent of air-sea coupling and primarily generated by the dynamics of the atmosphere. The southeastward propagation of rainfall anomalies associated with two leading rainfall modes in the South Pacific depends upon the eastward propagating </span><span>Madden-Julian Oscillation (</span><span>MJO</span><span>)</span><span> signals over the Indian Ocean and western Pacific. Air-sea interaction seems crucial for such propagation as both eastward and southeastward propagations substantially reduced in the uncoupled model forced with SST climatology. Prescribing daily SST from the coupled model improves the simulation of both eastward and southeastward propagations in the uncoupled model forced with daily SST, showing the role of SST variability on the propagation of the intraseasonal variability, but the periodicity differs from the coupled model. The change in the periodicity is attributed to a weaker SST-rainfall relationship that shifts from SST leading rainfall to a nearly in-phase relationship in the uncoupled model forced with daily SST.</span></p>

scholarly journals CMIP5-Based Spatiotemporal Changes of Extreme Temperature Events during 2021–2100 in Mainland China

2020 ◽  
Vol 12 (11) ◽  
pp. 4418
Author(s):  
Hong Ying ◽  
Hongyan Zhang ◽  
Ying Sun ◽  
Jianjun Zhao ◽  
Zhengxiang Zhang ◽  
...  
Keyword(s):  
Mainland China ◽  
Extreme Temperature ◽  
Coupled Model ◽  
Terrestrial Ecosystem ◽  
The Tibetan Plateau ◽  
Rcp Scenarios ◽  
Taylor Diagram ◽  
Intercomparison Project ◽  
Surrounding Areas ◽  
Climate Events

The increasing number of extreme climate events is having a great impact on the terrestrial ecosystem. In this study, we applied a Taylor diagram to evaluate the 7 extreme temperature indices (ETI) of 12 models and the multi-model ensemble (MME) mean from phase 5 of the Coupled Model Intercomparison Project (CMIP5) during 1961–2005, and found that the MME has the best simulation effect. Warm indices and warm duration indices increase slowly, rapidly, and extremely under the representative concentration pathway 2.6 (RCP2.6), RCP4.5, and RCP8.5 scenarios, respectively. In contrast, the decrease in cold indices and cold duration indices are slow, rapid and extreme, respectively. The ETI from 2021–2100 under the RCP2.6 and RCP4.5 scenarios have primary periods ranging from 1–16 years. Under the RCP2.6 and RCP4.5 scenarios, the changes of warm indices are relatively largest in the basin of the central, and southeastern, while, under the RCP8.5 scenario, the changes are relatively significant, except for basin of northeast. The cold indices have the most significant decreasing trend in the Tibetan Plateau and its surrounding areas, under the three RCP scenarios. The findings from this study can provide reference for the risk management and prevention of climate disasters in the context of climate change in mainland China.

scholarly journals Results of the Verification of the Statistical Distribution Model of Microseismicity Emission Characteristics

2016 ◽  
Vol 61 (3) ◽  
pp. 489-496
Author(s):  
Aleksander Cianciara
Keyword(s):  
Distribution Function ◽  
Weibull Distribution ◽  
Goodness Of Fit ◽  
Cumulative Distribution Function ◽  
Statistical Distribution ◽  
Cumulative Distribution ◽  
Distribution Model ◽  
Emission Characteristics ◽  
Goodness Of Fit Test ◽  
Empirical Cumulative Distribution Function

Abstract The paper presents the results of research aimed at verifying the hypothesis that the Weibull distribution is an appropriate statistical distribution model of microseismicity emission characteristics, namely: energy of phenomena and inter-event time. It is understood that the emission under consideration is induced by the natural rock mass fracturing. Because the recorded emission contain noise, therefore, it is subjected to an appropriate filtering. The study has been conducted using the method of statistical verification of null hypothesis that the Weibull distribution fits the empirical cumulative distribution function. As the model describing the cumulative distribution function is given in an analytical form, its verification may be performed using the Kolmogorov-Smirnov goodness-of-fit test. Interpretations by means of probabilistic methods require specifying the correct model describing the statistical distribution of data. Because in these methods measurement data are not used directly, but their statistical distributions, e.g., in the method based on the hazard analysis, or in that that uses maximum value statistics.

scholarly journals The Multidecadal Atlantic SST—Sahel Rainfall Teleconnection in CMIP5 Simulations

2014 ◽  
Vol 27 (2) ◽  
pp. 784-806 ◽  
Author(s):  
Elinor R. Martin ◽  
Chris Thorncroft ◽  
Ben B. B. Booth
Keyword(s):  
North Atlantic ◽  
Rainfall Variability ◽  
Coupled Model ◽  
Multidecadal Variability ◽  
The North ◽  
Sst Variability ◽  
Tropical North Atlantic ◽  
Sahel Rainfall ◽  
Intercomparison Project ◽  
The Indian Ocean

Abstract This study uses models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) to evaluate and investigate Sahel rainfall multidecadal variability and teleconnections with global sea surface temperatures (SSTs). Multidecadal variability is lower than observed in all historical simulations evaluated. Focus is on teleconnections with North Atlantic SST [Atlantic multidecadal variability (AMV)] as it is more successfully simulated than the Indian Ocean teleconnection. To investigate why some models successfully simulated this teleconnection and others did not, despite having similarly large AMV, two groups of models were selected. Models with large AMV were highlighted as good (or poor) by their ability to simulate relatively high (low) Sahel multidecadal variability and have significant (not significant) correlation between multidecadal Sahel rainfall and an AMV index. Poor models fail to capture the teleconnection between the AMV and Sahel rainfall because the spatial distribution of SST multidecadal variability across the North Atlantic is incorrect. A lack of SST signal in the tropical North Atlantic and Mediterranean reduces the interhemispheric SST gradient and, through circulation changes, the rainfall variability in the Sahel. This pattern was also evident in the control simulations, where SST and Sahel rainfall variability were significantly weaker than historical simulations. Errors in SST variability were suggested to result from a combination of weak wind–evaporation–SST feedbacks, poorly simulated cloud amounts and feedbacks in the stratocumulus regions of the eastern Atlantic, dust–SST–rainfall feedbacks, and sulfate aerosol interactions with clouds. By understanding the deficits and successes of CMIP5 historical simulations, future projections and decadal hindcasts can be examined with additional confidence.

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