Dominant spatial‐patterns of interannual variability in summer precipitation across northern Eurasia from CMIP5 models

2022 ◽  
Author(s):  
Manabu Abe ◽  
Hatsuki Fujinami ◽  
Tetsuya Hiyama
Keyword(s):  
Interannual Variability ◽  
Spatial Patterns ◽  
Summer Precipitation ◽  
Cmip5 Models ◽  
Northern Eurasia

Reproducibility of Summer Precipitation over Northern Eurasia in CMIP5 Multiclimate Models

2016 ◽  
Vol 29 (9) ◽  
pp. 3317-3337 ◽  
Author(s):  
Nagio Hirota ◽  
Yukari N. Takayabu ◽  
Atsushi Hamada
Keyword(s):  
Cloud Cover ◽  
Climate Models ◽  
Coupled Model ◽  
Lower Troposphere ◽  
Summer Precipitation ◽  
Eastern Coast ◽  
Cmip5 Models ◽  
Northern Eurasia ◽  
Western Eurasia ◽  
Precipitation Events

Abstract Reproducibility of summer precipitation over northern Eurasia in climate models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) is evaluated in comparison with several observational and reanalysis datasets. All CMIP5 models under- and overestimate precipitation over western and eastern Eurasia, respectively, and the reproducibility measured using the Taylor skill score is largely determined by the severity of these west–east precipitation biases. The following are the two possible causes for the precipitation biases: very little cloud cover and very strong local evaporation–precipitation coupling. The models underestimate cloud cover over Eurasia, allowing too much sunshine and leading to a warm bias at the surface. The associated cyclonic circulation biases in the lower troposphere weaken the modeled moisture transport from the Atlantic to western Eurasia and enhance the northward moisture flux along the eastern coast. Once the dry west and wet east biases appear in the models, they become amplified because of stronger evaporation–precipitation coupling. The CMIP5 models reproduce precipitation events well over a time scale of several days, including the associated low pressure systems and local convection. However, the modeled precipitation events are relatively weaker over western Eurasia and stronger over eastern Eurasia compared to the observations, and these are consistent with the biases found in the seasonal average fields.

Representation of the Angolan low and Southern African Summer Precipitation in the CORDEX and CMIP5 models. 

2021 ◽  
Author(s):  
Sabina Abba-Omar ◽  
Francesca Raffaele ◽  
Erika Coppola ◽  
Daniela Jacob ◽  
Claas Teichmann ◽  
...  
Keyword(s):  
Climate Change ◽  
Regression Analysis ◽  
Southern Africa ◽  
Summer Precipitation ◽  
Precipitation Variability ◽  
Cmip5 Models ◽  
Impact Of Climate Change ◽  
The Future ◽  
Rainfall Patterns ◽  
The Impact

<p>The impact of climate change on precipitation over Southern Africa is of particular interest due to its possible devastating societal impacts. To add to this, simulating precipitation is challenging and models tend to show strong biases over this region, especially during the Austral Summer (DJF) months. One of the reasons for this is the mis-representation of the Angolan Low (AL) and its influence on Southern Africa’s Summer precipitation in the models. Therefore, this study aims to explore and compare different models’ ability to capture the AL and its link to precipitation variability as well as consider the impact climate change may have on this link. We also explore how the interaction between ENSO, another important mode of variability for precipitation, and the Angolan Low, impact precipitation, how the models simulate this and whether this could change in the future under climate change. </p><p>We computed the position and strength of the AL in reanalysis data and compared these results to three different model ensembles with varying resolutions. Namely, the CORDEX-CORE ensemble (CCORE), a new phase of CORDEX simulations with higher resolutions (0.22 degrees), the lower resolution (0.44 degrees) CORDEX-phase 1 ensemble (C44) and the CMIP5 models that drive the two RCM ensembles. We also used Self Organizing Maps to group DJF yearly anomaly patterns and identify which combination of ENSO and AL strength scenarios are responsible for particularly wet or dry conditions. Regression analysis was performed to analyze the relationships between precipitation and the AL and ENSO. This analysis was repeated for near (2041-2060) and far (2080-2099) future climate and compared with the present to understand how the strength of the AL, and its connection to precipitation variability and ENSO, changes in the future. </p><p>We found that, in line with previous studies, models with stronger AL tend to produce more rainfall. CCORE tends to simulate a stronger AL than C44 and therefore, higher precipitation biases. However, the regression analysis shows us that CCORE is able to capture the relationship between precipitation and the AL strength variability as well as ENSO better than the other ensembles. We found that generally dry rainfall patterns over Southern Africa are associated with a weak AL and El Nino event whereas wet rainfall patterns occur during a strong AL and La Nina year. While the models are able to capture this, they also tend to show more neutral ENSO conditions associated with these wet and dry patterns which possibly indicates less of a connection between AL strength and ENSO than seen in the observed results. Analysis of the future results indicates that the AL weakens, this is shown across all the ensembles and could be a contributing factor to some of the drying seen. These results have applications in understanding and improving model representation of precipitation over Southern Africa as well as providing some insight into the impact of climate change on precipitation and some of its associated dynamics over this region.</p>

Climate Extremes of the 2019/2020 Winter in Northern Eurasia: Contributions by the Climate Trend and Interannual Variability Related to the Arctic Oscillation

2021 ◽  
pp. 5-16
Author(s):  
V. N. Kryjov ◽  
Keyword(s):  
Interannual Variability ◽  
Arctic Oscillation ◽  
Global Climate ◽  
Linear Trend ◽  
Climate Extremes ◽  
The Arctic ◽  
Northern Eurasia ◽  
Climate Trend ◽  
Temperature And Precipitation ◽  
The Arctic Oscillation

The 2019/2020 wintertime (December–March) anomalies of sea level pressure, temperature, and precipitation are analyzed. The contribution of the 40-year linear trend in these parameters associated with global climate change and of the interannual variability associated with the Arctic Oscillation (AO) is assessed. In the 2019/2020 winter, extreme zonal circulation was observed. The mean wintertime AO index was 2.20, which ranked two for the whole observation period (started in the early 20th century) and was outperformed only by the wintertime index of 1988/1989. It is shown that the main contribution to the 2019/2020 wintertime anomalies was provided by the AO. A noticeable contribution of the trend was observed only in the Arctic. Extreme anomalies over Northern Eurasia were mainly associated with the AO rather than the trend. However, the AO-related anomalies, particularly air temperature anomalies, were developing against the background of the trend-induced increased mean level.

Cyclone activity associated with the interannual seesaw oscillation of summer precipitation over northern Eurasia

2007 ◽  
Vol 56 (3-4) ◽  
pp. 387-398 ◽  
Author(s):  
Yoshiki Fukutomi ◽  
Kooiti Masuda ◽  
Tetsuzo Yasunari
Keyword(s):  
Summer Precipitation ◽  
Northern Eurasia ◽  
Cyclone Activity

scholarly journals Climate change projections of boreal summer precipitation over tropical America by using statistical downscaling from CMIP5 models

2017 ◽  
Vol 12 (12) ◽  
pp. 124011 ◽  
Author(s):  
Reiner Palomino-Lemus ◽  
Samir Córdoba-Machado ◽  
Sonia Raquel Gámiz-Fortis ◽  
Yolanda Castro-Díez ◽  
María Jesús Esteban-Parra
Keyword(s):  
Climate Change ◽  
Statistical Downscaling ◽  
Summer Precipitation ◽  
Cmip5 Models ◽  
Boreal Summer ◽  
Climate Change Projections ◽  
Tropical America

scholarly journals An Observational and Model Characterization of Vertical Structure of Wind Fields over Eastern United States: A Case Study of Sterling, Virginia

2016 ◽  
Vol 2016 ◽  
pp. 1-15
Author(s):  
Sium Gebremariam ◽  
Belay Demoz ◽  
Churchill Okonkwo ◽  
Ricardo K. Sakai
Keyword(s):  
Wind Speed ◽  
Vertical Distribution ◽  
Interannual Variability ◽  
Signal To Noise Ratio ◽  
Coupled Model ◽  
Cmip5 Models ◽  
Eastern United States ◽  
Wind Fields ◽  
Reanalysis Dataset ◽  
Radiosonde Observation

The performance of twenty GCMs that participated in the Coupled Model Intercomparison Phase 5 (CMIP5) is evaluated at Sterling, Virginia, by comparing model outputs with radiosonde observational dataset and reanalysis dataset. We evaluated CMIP5 models in their ability to simulate wind climatology, seasonal cycle, interannual variability, and trends at the pressure levels from 850 hPa to 30 hPa. We also addressed the question of the number of years required to detect statistically significant wind trends using radiosonde wind measurements. Our results show that CMIP5 models and reanalysis successfully reproduced the observed climatological annual mean zonal wind and wind speed vertical distribution. They also capture the observed seasonal zonal, meridional, and wind speed vertical distribution with stronger (weaker) wind during the winter (summer) season. However, there is some disagreement in the magnitude of vertical profiles among CMIP5 models, reanalysis, and radiosonde observation. Overall, the number of years to obtain statistically significant trend decreases with increasing pressure level except for upper troposphere. Although the vertical profile of interannual variability of CMIP5 models and reanalysis agree with the radiosonde observation, the wind trend is not statistically significant. This indicates that detection of trends on local scale is challenging because of small signal-to-noise ratio problems.

scholarly journals TES observations of the interannual variability of PAN over Northern Eurasia and the relationship to springtime fires

2015 ◽  
Vol 42 (17) ◽  
pp. 7230-7237 ◽  
Author(s):  
Liye Zhu ◽  
Emily V. Fischer ◽  
Vivienne H. Payne ◽  
John R. Worden ◽  
Zhe Jiang
Keyword(s):  
Interannual Variability ◽  
Northern Eurasia ◽  
The Relationship

Historical Land-Cover Change Impacts on Climate: Comparative Assessment of LUCID and CMIP5 Multimodel Experiments

2017 ◽  
Vol 30 (4) ◽  
pp. 1439-1459 ◽  
Author(s):  
Quentin Lejeune ◽  
Sonia I. Seneviratne ◽  
Edouard L. Davin
Keyword(s):  
Land Cover ◽  
Diurnal Cycle ◽  
Land Surface ◽  
Model Comparison ◽  
Forest Cover ◽  
Cmip5 Models ◽  
Northern Eurasia ◽  
Mean Temperature ◽  
Agricultural Areas ◽  
The Impact

Abstract During the industrial period, many regions experienced a reduction in forest cover and an expansion of agricultural areas, in particular North America, northern Eurasia, and South Asia. Here, results from the Land-Use and Climate, Identification of Robust Impacts (LUCID) and CMIP5 model intercomparison projects are compared in order to investigate how land-cover changes (LCC) in these regions have locally impacted the biophysical land surface properties, like albedo and evapotranspiration, and how this has affected seasonal mean temperature as well as its diurnal cycle. The impact of LCC is extracted from climate simulations, including all historical forcings, using a method that is shown to capture well the sign and the seasonal cycle of the impacts diagnosed from single-forcing experiments in most cases. The model comparison reveals that both the LUCID and CMIP5 models agree on the albedo-induced reduction of mean winter temperatures over midlatitudes. In contrast, there is less agreement concerning the response of the latent heat flux and, subsequently, mean temperature during summer, when evaporative cooling plays a more important role. Overall, a majority of models exhibit a local warming effect of LCC during this season, contrasting with results from the LUCID studies. A striking result is that none of the analyzed models reproduce well the changes in the diurnal cycle identified in present-day observations of the effect of deforestation. However, overall the CMIP5 models better simulate the observed summer daytime warming effect compared to the LUCID models, as well as the winter nighttime cooling effect.

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