scholarly journals Evaluation of Temperature and Precipitation Trends and Long-Term Persistence in CMIP5 Twentieth-Century Climate Simulations

2013 ◽  
Vol 26 (12) ◽  
pp. 4168-4185 ◽  
Author(s):  
Sanjiv Kumar ◽  
Venkatesh Merwade ◽  
James L. Kinter ◽  
Dev Niyogi

Abstract The authors have analyzed twentieth-century temperature and precipitation trends and long-term persistence from 19 climate models participating in phase 5 of the Coupled Model Intercomparison Project (CMIP5). This study is focused on continental areas (60°S–60°N) during 1930–2004 to ensure higher reliability in the observations. A nonparametric trend detection method is employed, and long-term persistence is quantified using the Hurst coefficient, taken from the hydrology literature. The authors found that the multimodel ensemble–mean global land–average temperature trend (0.07°C decade−1) captures the corresponding observed trend well (0.08°C decade−1). Globally, precipitation trends are distributed (spatially) at about zero in both the models and in the observations. There are large uncertainties in the simulation of regional-/local-scale temperature and precipitation trends. The models’ relative performances are different for temperature and precipitation trends. The models capture the long-term persistence in temperature reasonably well. The areal coverage of observed long-term persistence in precipitation is 60% less (32% of land area) than that of temperature (78%). The models have limited capability to capture the long-term persistence in precipitation. Most climate models underestimate the spatial variability in temperature trends. The multimodel ensemble–average trend generally provides a conservative estimate of local/regional trends. The results of this study are generally not biased by the choice of observation datasets used, including Climatic Research Unit Time Series 3.1; temperature data from Hadley Centre/Climatic Research Unit, version 4; and precipitation data from Global Historical Climatology Network, version 2.

Water ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 1516 ◽  
Author(s):  
Zhijie Ta ◽  
Yang Yu ◽  
Lingxiao Sun ◽  
Xi Chen ◽  
Guijin Mu ◽  
...  

The Coupled Model Intercomparison Project Phase 5 (CMIP5) provides data, which is widely used to assess global and regional climate change. In this study, we evaluated the ability of 37 global climate models (GCMs) of CMIP5 to simulate historical precipitation in Central Asia (CA). The relative root mean square error (RRMSE), spatial correlation coefficient, and Kling-Gupta efficiency (KGE) were used as criteria for evaluation. The precipitation simulation results of GCMs were compared with the Climatic Research Unit (CRU) precipitation in 1986–2005. Most models show a variety of precipitation simulation capabilities both spatially and temporally, whereas the top six models were identified as having good performance in CA, including HadCM3, MIROC5, MPI-ESM-LR, MPI-ESM-P, CMCC-CM, and CMCC-CMS. As the GCMs have large uncertainties in the prediction of future precipitation, it is difficult to find the best model to predict future precipitation in CA. Multi-Model Ensemble (MME) results can give a good simulation of precipitation, and are superior to individual models.


2011 ◽  
Vol 24 (19) ◽  
pp. 5108-5124 ◽  
Author(s):  
Liwei Jia ◽  
Timothy DelSole

A new statistical optimization method is used to identify components of surface air temperature and precipitation on six continents that are predictable in multiple climate models on multiyear time scales. The components are identified from unforced “control runs” of the Coupled Model Intercomparison Project phase 3 dataset. The leading predictable components can be calculated in independent control runs with statistically significant skill for 3–6 yr for surface air temperature and 1–3 yr for precipitation, depending on the continent, using a linear regression model with global sea surface temperature (SST) as a predictor. Typically, lag-correlation maps reveal that the leading predictable components of surface air temperature are related to two types of SST patterns: persistent patterns near the continent itself and an oscillatory ENSO-like pattern. The only exception is Europe, which has no significant ENSO relation. The leading predictable components of precipitation are significantly correlated with an ENSO-like SST pattern. No multiyear predictability of land precipitation could be verified in Europe. The squared multiple correlations of surface air temperature and precipitation for nonzero lags on each continent are less than 0.4 in the first year, implying that less than 40% of variations of the leading predictable component can be predicted from global SST. The predictable components describe the spatial structures that can be predicted on multiyear time scales in the absence of anthropogenic and natural forcing, and thus provide a scientific rationale for regional prediction on multiyear time scales.


Atmosphere ◽  
2020 ◽  
Vol 11 (9) ◽  
pp. 1005
Author(s):  
Rizwan Karim ◽  
Guirong Tan ◽  
Brian Ayugi ◽  
Hassen Babaousmail ◽  
Fei Liu

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.


2019 ◽  
Vol 2019 ◽  
pp. 1-18 ◽  
Author(s):  
Suchada Kamworapan ◽  
Chinnawat Surussavadee

This study evaluates the performances of all forty different global climate models (GCMs) that participate in the Coupled Model Intercomparison Project Phase 5 (CMIP5) for simulating climatological temperature and precipitation for Southeast Asia. Historical simulations of climatological temperature and precipitation of the 40 GCMs for the 40-year period of 1960–1999 for both land and sea and those for the century of 1901–1999 for land are evaluated using observation and reanalysis datasets. Nineteen different performance metrics are employed. The results show that the performances of different GCMs vary greatly. CNRM-CM5-2 performs best among the 40 GCMs, where its total error is 3.25 times less than that of GCM performing worst. The performance of CNRM-CM5-2 is compared with those of the ensemble average of all 40 GCMs (40-GCM-Ensemble) and the ensemble average of the 6 best GCMs (6-GCM-Ensemble) for four categories, i.e., temperature only, precipitation only, land only, and sea only. While 40-GCM-Ensemble performs best for temperature, 6-GCM-Ensemble performs best for precipitation. 6-GCM-Ensemble performs best for temperature and precipitation simulations over sea, whereas CNRM-CM5-2 performs best over land. Overall results show that 6-GCM-Ensemble performs best and is followed by CNRM-CM5-2 and 40-GCM-Ensemble, respectively. The total errors of 6-GCM-Ensemble, CNRM-CM5-2, and 40-GCM-Ensemble are 11.84, 13.69, and 14.09, respectively. 6-GCM-Ensemble and CNRM-CM5-2 agree well with observations and can provide useful climate simulations for Southeast Asia. This suggests the use of 6-GCM-Ensemble and CNRM-CM5-2 for climate studies and projections for Southeast Asia.


2019 ◽  
Vol 15 (3) ◽  
pp. 1099-1111 ◽  
Author(s):  
Francisco José Cuesta-Valero ◽  
Almudena García-García ◽  
Hugo Beltrami ◽  
Eduardo Zorita ◽  
Fernando Jaume-Santero

Abstract. Estimates of climate sensitivity from general circulation model (GCM) simulations still present a large spread despite the continued improvements in climate modeling since the 1970s. This variability is partially caused by the dependence of several long-term feedback mechanisms on the reference climate state. Indeed, state-of-the-art GCMs present a large spread of control climate states probably due to the lack of a suitable reference for constraining the climatology of preindustrial simulations. We assemble a new gridded database of long-term ground surface temperatures (LoST database) obtained from geothermal data over North America, and we explore its use as a potential reference for the evaluation of GCM preindustrial simulations. We compare the LoST database with observations from the Climate Research Unit (CRU) database, as well as with five past millennium transient climate simulations and five preindustrial control simulations from the third phase of the Paleoclimate Modelling Intercomparison Project (PMIP3) and the fifth phase of the Coupled Model Intercomparison Project (CMIP5). The database is consistent with meteorological observations as well as with both types of preindustrial simulations, which suggests that LoST temperatures can be employed as a reference to narrow down the spread of surface temperature climatologies on GCM preindustrial control and past millennium simulations.


2020 ◽  
Author(s):  
Baijun Tian

<p>The double-Intertropical Convergence Zone (ITCZ) bias is one of the most outstanding problems in climate models. This study seeks to examine the double-ITCZ bias in the latest state-of-the-art fully coupled global climate models that participated in Coupled Model Intercomparison Project (CMIP) Phase 6 (CMIP6) in comparison to their previous generations (CMIP3 and CMIP5 models). To that end, we have analyzed the long-term annual mean tropical precipitation distributions and several precipitation bias indices that quantify the double-ITCZ biases in 75 climate models including 24 CMIP3 models, 25 CMIP3 models, and 26 CMIP6 models. We find that the double-ITCZ bias and its big inter-model spread persist in CMIP6 models but the double-ITCZ bias is slightly reduced from CMIP3 or CMIP5 models to CMIP6 models.</p>


2020 ◽  
Vol 53 (2F) ◽  
pp. 1-17
Author(s):  
Safieh Javadinejad

In order to develop a valued decision-support system for climate alteration policy and planning, recognizing the regionally-specific features of the climate change, energy-water nexus, and the history of the current and possible future climate, water and energy supply systems is necessary. This paper presents an integrated climate change, water/energy modeling platform which allows tailored climate alteration and water-energy assessments. This modeling platform is established and described in details based on particular regional circumstances. The modeling platform involves linking three different models, including the climate change model from Coupled Model Intercomparison Project Phase 5 under the most severe scenario (Representative Concentration Pathways, Water Evaluation, and Planning system and the Long-range Energy Alternatives Planning system). This is to understand the impacts of climate variability (changes in temperature and precipitation) on water and electricity consumption in Zayandeh Rud River Basin (Central Iran) for the current (1971–2005) and future time period (2006–2040). Climate models have projected that the temperature will increase by 7 °C and precipitation will decrease by 44%, it is also proposed that electricity imports will rise during a severe dry scenario in the basin, while power generation will decrease around 8%.


2019 ◽  
Author(s):  
Takasumi Kurahashi-Nakamura ◽  
André Paul ◽  
Guy Munhoven ◽  
Ute Merkel ◽  
Michael Schulz

Abstract. We developed a coupling scheme for the Community Earth System Model version 1.2 (CESM1.2) and the Model of Early Diagenesis in the Upper Sediment of Adjustable complexity (MEDUSA), and explored the effects of the coupling on solid components in the upper sediment and on bottom seawater chemistry by comparing the coupled model's behaviour with that of the uncoupled CESM having a simplified treatment of sediment processes. CESM is a fully-coupled atmosphere-ocean-sea ice-land model and its ocean component (the Parallel Ocean Program version 2, POP2) includes a biogeochemical component (BEC). MEDUSA was coupled to POP2 in an off-line manner so that each of the models ran separately and sequentially with regular exchanges of necessary boundary condition fields. This development was done with the ambitious aim of a future application for long-term (spanning a full glacial cycle; i.e., ~ 105 years) climate simulations with a state-of-the-art comprehensive climate model including the carbon cycle, and was motivated by the fact that until now such simulations have been done only with less-complex climate models. We found that the sediment-model coupling already had non-negligible immediate advantages for ocean biogeochemistry in millennial-time-scale simulations. First, the MEDUSA-coupled CESM outperformed the uncoupled CESM in reproducing an observation-based global distribution of sediment properties, especially for organic carbon and opal. Thus, the coupled model is expected to act as a better bridge between climate dynamics and sedimentary data, which will provide another measure of model performance. Second, in our experiments, the MEDUSA-coupled model and the uncoupled model had a difference of 0.2‰ or larger in terms of δ13C of bottom water over large areas, which implied potential significant model biases for bottom seawater chemical composition due to a different way of sediment treatment. Such a model bias would be a fundamental issue for paleo model–data comparison often relying on data derived from benthic foraminifera.


2021 ◽  
Author(s):  
Jiao Li ◽  
Yang Zhao ◽  
Deliang Chen ◽  
Yanzhen Kang ◽  
Hui Wang

AbstractPrevious studies have projected an increase in future summer precipitation across East Asia (EA). This study investigates the relative contributions of thermodynamic and dynamic components to future precipitation changes in three key sub-regions of EA where the maximum centers of the historical precipitation are located (the tropical region, East China, and the Japan and Korea sector), and analyzes the causes of the changes in thermodynamic and dynamic components. Outputs from 30 climate models of the Coupled Model Intercomparison Project Phase 6 (CMIP6) are used. From these, the five best-performing models for historical summer precipitation climatology for EA are selected. The future summer precipitations in the three sub-regions over the near- to mid-term (2020–2069) and the long-term (2070–2095) are then examined using the multi-model ensemble mean of the five models selected (MMM05). The projections were driven by four combined scenarios of the Shared Socioeconomic Pathways (SSPs) and forcing levels of the Representative Concentration Pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). The results show that long-term precipitations under SSP5-8.5 are greater than those under the other scenarios across all sub-regions. After the 2070s under SSP5-8.5, a marked precipitation intensification is identified in all three sub-regions, but with different rates of increase. The projected precipitation increase is primarily attributed to the thermodynamic component, while the dynamic component related to circulation changes is relatively weak. Further analysis indicates that the pattern of the thermodynamic component in the three sub-regions is dominated by the climatological upward motion, mediated by an increase in moisture.


2022 ◽  
Author(s):  
Mohammad Naser Sediqi ◽  
Vempi Satriya Adi Hendrawan ◽  
Daisuke Komori

Abstract The global climate models (GCMs) of Coupled Model Intercomparison Project phase 6 (CMIP6) were used spatiotemporal projections of precipitation and temperature over Afghanistan for three shared socioeconomic pathways (SSP1-2.6, 2-4.5 and 5-8.5) and two future time horizons, early (2020-2059) and late (2060-2099). The Compromise Programming (CP) approach was employed to order the GCMs based on their skill to replicate precipitation and temperature climatology for the reference period (1975-2014). Three models, namely ACCESS-CM2, MPI-ESM1-2-LR, and FIO-ESM-2-0, showed the highest skill in simulating all three variables, and therefore, were chosen for the future projections. The ensemble mean of the GCMs showed an increase in maximum temperature by 1.5-2.5oC, 2.7-4.3 oC, and 4.5-5.3 oC and minimum temperature by 1.3-1.8 oC, 2.2-3.5 oC, and 4.6-5.2 oC for SSP1-2.6, SSP2-4.5, and SSP5-8.5, respectively in the later period. Meanwhile, the changes in precipitation in the range of -15-18%, -36-47% and -40-68% for SSP1-2.6, SSP2-4.5, and SSP5-8.5, respectively. The temperature and precipitation were projected to increase in the highlands and decrease over the deserts, indicating dry regions would be drier and wet regions wetter.


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