Prediction of extreme events in precipitation and temperature over CONUS during boreal summer in the UFS coupled model

2022 ◽  
Author(s):  
V. Krishnamurthy ◽  
Cristiana Stan
Keyword(s):  
Extreme Events ◽  
Coupled Model ◽  
Boreal Summer ◽  
Precipitation And Temperature

Predictability of extreme events in UFS at subseasonal time scale

2021 ◽  
Author(s):  
Christiana Stan
Keyword(s):  
Extreme Events ◽  
Time Scale ◽  
Initial Conditions ◽  
Coupled Model ◽  
Forecast Skill ◽  
Boreal Summer ◽  
Boreal Winter ◽  
Forecast System ◽  
Cold Events ◽  
Second Category

<p>The predictability of extreme events over the continental United States (CONUS) in the Unified Forecast System (UFS) Couple Model is studied at subseasonal time scale. The benchmark runs of UFS (GFSv15), a coupled model consisting of atmospheric component (FV3GFS) with 28 km resolution and ocean (MOM6) and sea ice (CICE5) components with global 0.25° resolution, for the period April 2011–December 2017 have been assessed. The model’s month-long forecasts initiated on the first and fifteenth of each month are used to examine the predictability of extreme events in precipitation and 2m temperature. The atmospheric and ice initial conditions are from CFSR data, and the ocean initial conditions are from 3Dvar CPC. The errors in the week 1–4 predictions and the corresponding spatial correlation between model and observation over CONUS are presented. The differences in the predictability of the extreme events between the boreal summer and winter are discussed. Two categories of extreme events are evaluated: 95<sup>th</sup> and 99<sup>th</sup> percentile, respectively. The forecast skill of extreme events in the 95<sup>th</sup> percentile is higher than the forecast skill of events in the second category. The forecast skill of warm and cold events in the 95<sup>th</sup> percentile shows seasonal dependence and is higher during the boreal winter.</p>

scholarly journals Evaluating Boreal Summer Circulation Patterns of CMIP6 Climate Models Over The Asian Region

2021 ◽  
Author(s):  
Lulei Bu ◽  
Zhiyan Zuo ◽  
Ning An
Keyword(s):  
Extreme Events ◽  
Climate Models ◽  
Model Performance ◽  
Coupled Model ◽  
Boreal Summer ◽  
Self Organizing Map ◽  
Summer Circulation ◽  
Circulation Patterns ◽  
Daily Scale ◽  
Intercomparison Project

Abstract Our confidence in future climate projection depends on the ability of climate models to simulate the current climate, and model performance in simulating atmospheric circulation affects the ability to simulate extreme events. This study uses the self-organizing map (SOM) method to evaluate the frequency, persistence, and transition characteristics of models in the Coupled Model Intercomparison Project Phase 6 (CMIP6) for different ensembles of the 500 hPa daily geopotential height (Z500) in Asia, and then ranks all ensembles according to a comprehensive ranking metric (MR). Our results show that the SOM method is a powerful tool for assessing the daily-scale circulation simulation skills in Asia, and the results are not significantly affected by different map sizes. Positive associations between the performance of ensembles for any two of frequency, persistence, and transition were found, indicating that an ensemble that performs well for one metric is good for the others. The results of the MR ranking show that the r10i1p1f1 ensemble of CanESM5 gives the best overall simulation of 500 hPa circulation in Asia, and this is also the ensemble that best simulates frequency characteristics. The MR simulation skills of the 10 best ensembles for the position of the Western North Pacific Subtropical High (WNPSH) are far better than those of the 10 worst. Such differences may lead to errors in the simulation of extreme events. This study will help future studies in the choice of ensembles with higher circulation simulation skills to improve the credibility of their conclusions.

scholarly journals Future Changes in Simulated Evapotranspiration across Continental Africa Based on CMIP6 CNRM-CM6

2021 ◽  
Vol 18 (13) ◽  
pp. 6760
Author(s):  
Isaac Kwesi Nooni ◽  
Daniel Fiifi T. Hagan ◽  
Guojie Wang ◽  
Waheed Ullah ◽  
Jiao Lu ◽  
...  
Keyword(s):  
Extreme Events ◽  
Coupled Model ◽  
Baseline Period ◽  
Model Intercomparison ◽  
Additional Information ◽  
Intercomparison Project ◽  
Key Drivers ◽  
Model Ensembles ◽  
Projected Changes ◽  
Near Future

The main goal of this study was to assess the interannual variations and spatial patterns of projected changes in simulated evapotranspiration (ET) in the 21st century over continental Africa based on the latest Shared Socioeconomic Pathways and the Representative Concentration Pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) provided by the France Centre National de Recherches Météorologiques (CNRM-CM) model in the Sixth Phase of Coupled Model Intercomparison Project (CMIP6) framework. The projected spatial and temporal changes were computed for three time slices: 2020–2039 (near future), 2040–2069 (mid-century), and 2080–2099 (end-of-the-century), relative to the baseline period (1995–2014). The results show that the spatial pattern of the projected ET was not uniform and varied across the climate region and under the SSP-RCPs scenarios. Although the trends varied, they were statistically significant for all SSP-RCPs. The SSP5-8.5 and SSP3-7.0 projected higher ET seasonality than SSP1-2.6 and SSP2-4.5. In general, we suggest the need for modelers and forecasters to pay more attention to changes in the simulated ET and their impact on extreme events. The findings provide useful information for water resources managers to develop specific measures to mitigate extreme events in the regions most affected by possible changes in the region’s climate. However, readers are advised to treat the results with caution as they are based on a single GCM model. Further research on multi-model ensembles (as more models’ outputs become available) and possible key drivers may provide additional information on CMIP6 ET projections in the region.

scholarly journals Tendency Bias Correction in Coupled and Uncoupled Global Climate Models with a Focus on Impacts over North America

2019 ◽  
Vol 32 (2) ◽  
pp. 639-661 ◽  
Author(s):  
Y. Chang ◽  
S. D. Schubert ◽  
R. D. Koster ◽  
A. M. Molod ◽  
H. Wang
Keyword(s):  
North America ◽  
Bias Correction ◽  
Climate Models ◽  
Global Climate ◽  
Storm Track ◽  
Coupled Model ◽  
Global Climate Models ◽  
Forecast Skill ◽  
Boreal Summer ◽  
Transient Wave

Abstract We revisit the bias correction problem in current climate models, taking advantage of state-of-the-art atmospheric reanalysis data and new data assimilation tools that simplify the estimation of short-term (6 hourly) atmospheric tendency errors. The focus is on the extent to which correcting biases in atmospheric tendencies improves the model’s climatology, variability, and ultimately forecast skill at subseasonal and seasonal time scales. Results are presented for the NASA GMAO GEOS model in both uncoupled (atmosphere only) and coupled (atmosphere–ocean) modes. For the uncoupled model, the focus is on correcting a stunted North Pacific jet and a dry bias over the central United States during boreal summer—long-standing errors that are indeed common to many current AGCMs. The results show that the tendency bias correction (TBC) eliminates the jet bias and substantially increases the precipitation over the Great Plains. These changes are accompanied by much improved (increased) storm-track activity throughout the northern midlatitudes. For the coupled model, the atmospheric TBCs produce substantial improvements in the simulated mean climate and its variability, including a much reduced SST warm bias, more realistic ENSO-related SST variability and teleconnections, and much improved subtropical jets and related submonthly transient wave activity. Despite these improvements, the improvement in subseasonal and seasonal forecast skill over North America is only modest at best. The reasons for this, which are presumably relevant to any forecast system, involve the competing influences of predictability loss with time and the time it takes for climate drift to first have a significant impact on forecast skill.

scholarly journals Projected changes in temperature and precipitation over mainland Southeast Asia by CMIP6 models

2021 ◽  
Author(s):  
S. Supharatid ◽  
J. Nafung ◽  
T. Aribarg
Keyword(s):  
Climate Change ◽  
Coupled Model ◽  
Southwest Monsoon ◽  
Future Climate Change ◽  
Boreal Summer ◽  
Northeast Monsoon ◽  
Adaptation Measures ◽  
Mitigation And Adaptation ◽  
Temperature And Precipitation ◽  
Projected Changes

Abstract Five mainland SEA countries (Cambodia, Laos, Myanmar, Vietnam, and Thailand) are threatened by climate change. Here, the latest 18 Coupled Model Intercomparison Project Phase 6 (CMIP6) is employed to examine future climate change in this region under two SSP-RCP (shared socioeconomic pathway-representative concentration pathway) scenarios (SSP2-4.5 and SSP5-8.5). The bias-corrected multi-model ensemble (MME) projects a warming (wetting) over Cambodia, Laos, Myanmar, Vietnam, and Thailand by 1.88–3.89, 2.04–4.22, 1.88–4.09, 2.03–4.25, and 1.90–3.96 °C (8.76–20.47, 12.69–21.10, 9.54–21.10, 13.47–22.12, and 7.03–15.17%) in the 21st century with larger values found under SSP5-8.5 than SSP2-4.5. The MME model displays approximately triple the current rainfall during the boreal summer. Overall, there are robust increases in rainfall during the Southwest Monsoon (3.41–3.44, 8.44–9.53, and 10.89–17.59%) and the Northeast Monsoon (−2.58 to 0.78, −0.43 to 2.81, and 2.32 to 5.45%). The effectiveness of anticipated climate change mitigation and adaptation strategies under SSP2-4.5 results in slowing down the warming trends and decreasing precipitation trends after 2050. All these findings imply that member countries of mainland SEA need to prepare for appropriate adaptation measures in response to the changing climate.

Forced modulations of Sahel rainfall at decadal timescale over the 20th Century.

2021 ◽  
Author(s):  
Cassien Diabe Ndiaye ◽  
Juliette Mignot ◽  
Elsa Mohino
Keyword(s):  
North Atlantic Ocean ◽  
Coupled Model ◽  
Internal Variability ◽  
Boreal Summer ◽  
Semiarid Region ◽  
Rainfall Regime ◽  
Anthropogenic Aerosols ◽  
Coupled Models ◽  
External Forcings ◽  
Sahel Precipitation

<p>The semiarid region of the Sahel was marked during the 20<sup>th</sup> Century by significant modulations of its rainfall regime. Part of these modulations has been associated with the internal variability of the climate system, mediated by changes in oceanic sea surface temperature (SST). We show here that the external forcings, and in particular anthropogenic aerosols, might have played a role more important than previously thought in setting these variations. The study is based on the recent simulations performed for CMIP6 with the IPSL-CM6A-LR coupled model. As in most coupled models, the maximum precipitation is limited to the southern Sahel during boreal summer in the IPSL-CM6A-LR model. A novel definition of the Sahel precipitation region is proposed in order to take this bias into account. Our results show that external forcings induce decadal modulations of Sahel precipitation that correlate significantly at 0.6 with the observed precipitations and that the anthropogenic aerosols explain more than 70% of these modulations. These results confirm recent results of CMIP6 highlighting an important role of aerosol forcing for the decadal climate in and around the North Atlantic ocean.</p>

scholarly journals Climate change projections over Indus basin using CMIP6 model simulations

2021 ◽  
Author(s):  
KOTESWARARAO KUNDETI ◽  
Lakshmi Kumar T.V ◽  
Ashwini Kulkarni ◽  
Chowdary J.S ◽  
Srinivas Desamsetti
Keyword(s):  
Climate Change ◽  
21St Century ◽  
Minimum Temperature ◽  
Coupled Model ◽  
Baseline Period ◽  
Indus Basin ◽  
Climate Indices ◽  
Data Sets ◽  
Projected Changes ◽  
Precipitation And Temperature

Abstract Indus basin is one of the most vulnerable regions due to climate change. This article presents the projected changes in precipitation and temperature over the Indus Basin using statistically downscaled, bias-corrected Coupled Model Intercomparison Project-6 (CMIP6) data sets for different shared socioeconomic pathways (SSP2-4.5 and SSP5-8.5) in response to global warming. The future changes in precipitation and temperature extremes for different epochal periods of the 21st century are outlined. The spatial variations of precipitation, maximum and minimum temperature obtained from the Multi-Model Mean (MMM) of CMIP6 models showed a good agreement with observations such as APHRODITE (precipitation), CPC (temperature) for the base period 1995 to 2014 over the Indus Basin. Our results suggest that there is a general increase in precipitation/ maximum and minimum temperature over the Upper Indus Basin/Lower Indus Basin by the end of the 21st century. It is also noted that the spatial variability of extreme climate indices is high during June to September (JJAS) than December to January (DJF). By the end of the century projections show that the precipitation changes are about 85% in JJAS and 40% in DJF with reference to the baseline (1995–2014) period over Indus Basin region. The temperature extreme indices are also increasing in future compare to the baseline period.

scholarly journals Climate Change is Likely to Alter Sheep and Goat Distributions in Mainland China

2021 ◽  
Vol 9 ◽  
Author(s):  
Yongxiang Zhang ◽  
Guogang Wang ◽  
Yu Zhang ◽  
Sicheng Zhao ◽  
Chengji Han
Keyword(s):  
Climate Change ◽  
Density Distribution ◽  
Mainland China ◽  
Coupled Model ◽  
Correlation Coefficients ◽  
Northwest China ◽  
Climate Data ◽  
Market Density ◽  
Precipitation And Temperature ◽  
Livestock Husbandry

Climate change endangers food security worldwide, especially in developing countries. Livestock husbandry is one of the essential livelihoods for farmers and herders in remote arid and semiarid regions. However, it remains unclear how climate change will impact livestock husbandry in the future. This study collected sheep and goat distributions from the “gridded livestock of the world” (GLW) dataset for 1943 counties in Mainland China. Current climate data include precipitation and temperature from the National Meteorological Information Center (NMIC). We disentangled the effects of precipitation and temperature on current distributions of sheep and goats with the Bayesian Hierarchical Model by Integrated Nest Laplace Approximation (INLA). Further, we forecasted the potential sheep and goat distributions in 2030 and 2050 under Coupled Model Intercomparison Project (CMIP) scenarios. Our result showed that sheep distribution is significantly correlated with elevation, slope, market density, and highway distance, with absolute correlation coefficients ranging from 0.019 to 0.411. In addition to elevation, slope, and market density, goat distribution is also affected by gain production, with a correlation coefficient of 0.055. There is a dynamic correlation of temperature and precipitation with sheep and goat density. The sheep density distribution is predicted to increase in Northwest China, while the goat density distribution might increase in farming areas under climate change. Finally, this study suggests for the sheep and goat breeding industry to respond to climate change.

scholarly journals Extreme Weather Events and Its Impacts on Rice Production in Coastal Odisha Region of India

2021 ◽  
Author(s):  
S Vijayakumar ◽  
A.K. Nayak ◽  
N. Manikandan ◽  
Suchismita Pattanaik ◽  
Rahul Tripathi ◽  
...  
Keyword(s):  
Grain Yield ◽  
Extreme Events ◽  
Heavy Precipitation ◽  
Extreme Weather ◽  
Significant Trend ◽  
Extreme Weather Events ◽  
Positive Trend ◽  
Rice Grain ◽  
Weather Events ◽  
Precipitation And Temperature

Abstract The study investigates trend in extreme daily precipitation and temperature over coastal Odisha, India. 18 weather indices (8 related to temperature and 10 related to rainfall) were calculated using RClimDex software package for the period 1980–2010 . Trend analysis was carried out using linear regression and non-parametric Mann-Kendall test to find out the statistical significance of various indices. Results indicated, a strong and significant trend in temperature indices while the weak and non-significant trend in precipitation indices. The positive trend in Tmax mean, Tmin mean, TN90p (warm nights), TX90p (warm days), diurnal temperature range (DTR), warm spell duration indicator (WSDI), consecutive dry days (CDD) indicates increasing the frequency of warming events in coastal Odisha. Similarly, positive trend in highest maximum 1-day precipitation (RX1), highest maximum 2 consecutive day precipitation (RX2), highest maximum 3 consecutive day precipitation (RX3), highest maximum 5 consecutive day precipitation (RX5), number of heavy precipitation days (≥64.5mm), number of very heavy precipitation days (≥124.5mm) and negative trend in the number of rainy days (R2.5mm), consecutive wet days (CWD) indicate changes toward the more intense and poor distribution of precipitation in coastal Odisha. The combined effect of precipitation and temperature extreme events showed negative effects on rice grain yield. With the increasing number of extreme events there was sharp decline in rice grain yield was observed in the same year in all the coastal districts. This study emphasizes the need for new technology/management practice to minimize the impacts of extreme weather events on rice yield.

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