scholarly journals Climatological Drought Forecasting Using Bias Corrected CMIP6 Climate Data: A Case Study for India

Forecasting ◽  
2020 ◽  
Vol 2 (2) ◽  
pp. 59-84 ◽  
Author(s):  
Alen Shrestha ◽  
Md Mafuzur Rahaman ◽  
Ajay Kalra ◽  
Rohit Jogineedi ◽  
Pankaj Maheshwari
Keyword(s):  
Coupled Model ◽  
Severity Index ◽  
Extreme Drought ◽  
Drought Severity ◽  
Climate Data ◽  
Drought Forecasting ◽  
Drought Duration ◽  
Intercomparison Project ◽  
Near Future

This study forecasts and assesses drought situations in various regions of India (the Araveli region, the Bundelkhand region, and the Kansabati river basin) based on seven simulated climates in the near future (2015–2044). The self-calibrating Palmer Drought Severity Index (scPDSI) was used based on its fairness in identifying drought conditions that account for the temperature as well. Gridded temperature and rainfall data of spatial resolution of 1 km were used to bias correct the multi-model ensemble mean of the Global Climatic Models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) project. Equidistant quantile-based mapping was adopted to remove the bias in the rainfall and temperature data, which were corrected on a monthly scale. The outcome of the forecast suggests multiple severe-to-extreme drought events of appreciable durations, mostly after the 2030s, under most climate scenarios in all the three study areas. The severe-to-extreme drought duration was found to last at least 20 to 30 months in the near future in all three study areas. A high-resolution drought index was developed and proven to be a key to assessing the drought situation.

CMIP6 Model-projected Hydroclimatic and Drought Changes and Their Causes in the 21st Century

2021 ◽  
pp. 1-58
Author(s):  
Tianbao Zhao ◽  
Aiguo Dai
Keyword(s):  
Palmer Drought Severity Index ◽  
Potential Evapotranspiration ◽  
Coupled Model ◽  
Severity Index ◽  
Distribution Functions ◽  
Drought Severity ◽  
Drought Duration ◽  
Probability Distribution Functions ◽  
Underlying Causes ◽  
The Mean

AbstractDrought is projected to become more severe and widespread as global warming continues in the 21st century, but hydroclimatic changes and their drivers are not well examined in the latest projections from the Phase Six of the Coupled Model Inetercomparison Project (CMIP6). Here, precipitation (P), evapotranspiration (E), soil moisture (SM), and runoff (R) from 25 CMIP6 models, together with self-calibrated Palmer Drought Severity Index with Penman-Monteith potential evapotranspiration (scPDSIpm), are analyzed to quantify hydroclimatic and drought changes in the 21st century and the underlying causes. Results confirm consistent drying in these hydroclimatic metrics across most of the Americas (including the Amazon), Europe and the Mediterranean region, southern Africa, and Australia; although the drying magnitude differs, with the drying being more severe and widespread in surface SM than in total SM. Global drought frequency based on surface SM and scPDSIpm increases by ~25%–100% (50%–200%) under the SSP2-4.5 (SSP5-8.5) scenario in the 21st century together with large increases in drought duration and areas, which result from a decrease in the mean and flattening of the probability distribution functions of SM and scPDSIpm; while the R-based drought changes are relatively small. Changes in both P and E contribute to the SM change, whereas scPDSIpm decreases result from ubiquitous PET increases and P decreases over subtropical areas. The R changes are determined primarily by P changes, while the PET change explains most of the E increase. Inter-model spreads in surface SM and R changes are large, leading to large uncertainties in the drought projections.

scholarly journals Robustness of the Link between Precipitation in North Africa and Standard Modes of Atmospheric Variability during the Last Millennium

Climate ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. 62
Author(s):  
Arab Djebbar ◽  
Hugues Goosse ◽  
François Klein
Keyword(s):  
North Africa ◽  
Coupled Model ◽  
Severity Index ◽  
Winter Precipitation ◽  
Precipitation Variability ◽  
Western Mediterranean ◽  
Drought Severity ◽  
Last Millennium ◽  
Intercomparison Project ◽  
Mediterranean Oscillation

Drought is a recurring phenomenon in North Africa, and extended dry periods can have a serious impact on economic and social structures, as well as the natural environment. Consequently, understanding the mechanisms that underlie precipitation variability in the region is a key driver of sustainable economic growth in activities such as agriculture, manufacturing, energy, and transport. North Africa’s climate differs significantly between coastal and inland areas. The region has a Mediterranean climate along the coast, characterized by mild, wet winters and warm, dry summers with reasonable rainfall of around 400 to 600 mm per year. The link between winter precipitation variability in this region and atmospheric patterns is assessed here using several gridded datasets of observations and reanalysis as well as model simulations from the fifth phase of the Coupled Model Intercomparison Project (CMIP5) and the third phase of the Paleoclimate Modelling Intercomparison Project (PMIP) covering the last millennium. Results show that the link between the zonal wind index at 850 hPa (U850) and winter precipitation is stronger and more robust over time than the link with some well-known modes of variability, such as the North Atlantic Oscillation (NAO), Mediterranean Oscillation (MO), and Western Mediterranean Oscillation (WeMO). U850 better explains the interannual changes in winter precipitation variability in North Africa for the past decades as well as the last millennium. Both winter precipitation and U850 simulated time series present significant decreasing trends, associated with drier conditions, starting in the 19th century. This is in agreement with the reconstructed and simulated Palmer Drought Severity Index (PDSI), which shows a decreasing trend toward drying conditions in North Africa.

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 A copula-based assessment of Bartlett–Lewis type of rainfall models for preserving drought statistics

2013 ◽  
Vol 10 (6) ◽  
pp. 7469-7516 ◽  
Author(s):  
M. T. Pham ◽  
W. J. Vanhaute ◽  
S. Vandenberghe ◽  
B. De Baets ◽  
N. E. C. Verhoest
Keyword(s):  
Time Series ◽  
Important Variable ◽  
Drought Severity ◽  
Test Case ◽  
Rainfall Time Series ◽  
Climate Data ◽  
Drought Duration ◽  
Environmental Consequences ◽  
Stochastic Point Process ◽  
The Impact

Abstract. Of all natural disasters, the economic and environmental consequences of droughts are among the highest because of their longevity and widespread spatial extent. Because of their extreme behaviour, studying droughts generally requires long time series of historical climate data. Rainfall is a very important variable for calculating drought statistics, for quantifying historical droughts or for assessing the impact on other hydrological (e.g. water stage in rivers) or agricultural (e.g. irrigation requirements) variables. Unfortunately, time series of historical observations are often too short for such assessments. To circumvent this, one may rely on the synthetic rainfall time series from stochastic point process rainfall models, such as Bartlett–Lewis models. The present study investigates whether drought statistics are preserved when simulating rainfall with Bartlett–Lewis models. Therefore, a 105 yr 10 min rainfall time series obtained at Uccle, Belgium is used as test case. First, drought events were identified on the basis of the Effective Drought Index (EDI), and each event was characterized by two variables, i.e. drought duration (D) and drought severity (S). As both parameters are interdependent, a multivariate distribution function, which makes use of a copula, was fitted. Based on the copula, four types of drought return periods are calculated for observed as well as simulated droughts and are used to evaluate the ability of the rainfall models to simulate drought events with the appropriate characteristics. Overall, all Bartlett–Lewis type of models studied fail in preserving extreme drought statistics, which is attributed to the model structure and to the model stationarity caused by maintaining the same parameter set during the whole simulation period.

scholarly journals Effects of Ocean Slow Response under Low Warming Targets

2020 ◽  
Vol 33 (2) ◽  
pp. 477-496 ◽  
Author(s):  
Shang-Min Long ◽  
Shang-Ping Xie ◽  
Yan Du ◽  
Qinyu Liu ◽  
Xiao-Tong Zheng ◽  
...  
Keyword(s):  
Radiative Forcing ◽  
Coupled Model ◽  
Deep Ocean ◽  
Ocean Warming ◽  
Meridional Overturning Circulation ◽  
Slow Response ◽  
Intercomparison Project ◽  
Meridional Overturning ◽  
Near Future

AbstractThe 2015 Paris Agreement proposed targets to limit global-mean surface temperature (GMST) rise well below 2°C relative to preindustrial level by 2100, requiring a cease in the radiative forcing (RF) increase in the near future. In response to changing RF, the deep ocean responds slowly (ocean slow response), in contrast to the fast ocean mixed layer adjustment. The role of the ocean slow response under low warming targets is investigated using representative concentration pathway (RCP) 2.6 simulations from phase 5 of the Coupled Model Intercomparison Project. In RCP2.6, the deep ocean continues to warm while RF decreases after reaching a peak. The deep ocean warming helps to shape the trajectories of GMST and fuels persistent thermosteric sea level rise. A diagnostic method is used to decompose further changes after the RF peak into a slow warming component under constant peak RF and a cooling component due to the decreasing RF. Specifically, the slow warming component amounts to 0.2°C (0.6°C) by 2100 (2300), raising the hurdle for achieving the low warming targets. When RF declines, the deep ocean warming takes place in all basins but is the most pronounced in the Southern Ocean and Atlantic Ocean where surface heat uptake is the largest. The climatology and change of meridional overturning circulation are both important for the deep ocean warming. To keep the GMST rise at a low level, substantial decrease in RF is required to offset the warming effect from the ocean slow response.

scholarly journals Projected Spatiotemporal Dynamics of Drought under Global Warming in Central Asia

2019 ◽  
Vol 11 (16) ◽  
pp. 4421 ◽  
Author(s):  
Zhang ◽  
Zhao ◽  
Ma ◽  
Brindha ◽  
Han ◽  
...  
Keyword(s):  
Global Warming ◽  
Central Asia ◽  
Social Life ◽  
Climate Model ◽  
Severity Index ◽  
Drought Severity ◽  
Warming Scenario ◽  
Intercomparison Project ◽  
Marked Decline ◽  
Human Social Life

Drought, one of the most common natural disasters that have the greatest impact on human social life, has been extremely challenging to accurately assess and predict. With global warming, it has become more important to make accurate drought predictions and assessments. In this study, based on climate model data provided by the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP), we used the Palmer Drought Severity Index (PDSI) to analyze and project drought characteristics and their trends under two global warming scenarios—1.5 °C and 2.0 °C—in Central Asia. The results showed a marked decline in the PDSI in Central Asia under the influence of global warming, indicating that the drought situation in Central Asia would further worsen under both warming scenarios. Under the 1.5 °C warming scenario, the PDSI in Central Asia decreased first and then increased, and the change time was around 2080, while the PDSI values showed a continuous decline after 2025 in the 2.0 °C warming scenario. Under the two warming scenarios, the spatial characteristics of dry and wet areas in Central Asia are projected to change significantly in the future. In the 1.5 °C warming scenario, the frequency of drought and the proportion of arid areas in Central Asia were significantly higher than those under the 2.0 °C warming scenario. Using the Thornthwaite (TH) formula to calculate the PDSI produced an overestimation of drought, and the Penman–Monteith (PM) formula is therefore recommended to calculate the index.

scholarly journals An assessment of the ability of Bartlett–Lewis type of rainfall models to reproduce drought statistics

2013 ◽  
Vol 17 (12) ◽  
pp. 5167-5183 ◽  
Author(s):  
M. T. Pham ◽  
W. J. Vanhaute ◽  
S. Vandenberghe ◽  
B. De Baets ◽  
N. E. C. Verhoest
Keyword(s):  
Time Series ◽  
Important Variable ◽  
Drought Severity ◽  
Test Case ◽  
Rainfall Time Series ◽  
Climate Data ◽  
Drought Duration ◽  
Environmental Consequences ◽  
Stochastic Point Process ◽  
The Impact

Abstract. Of all natural disasters, the economic and environmental consequences of droughts are among the highest because of their longevity and widespread spatial extent. Because of their extreme behaviour, studying droughts generally requires long time series of historical climate data. Rainfall is a very important variable for calculating drought statistics, for quantifying historical droughts or for assessing the impact on other hydrological (e.g. water stage in rivers) or agricultural (e.g. irrigation requirements) variables. Unfortunately, time series of historical observations are often too short for such assessments. To circumvent this, one may rely on the synthetic rainfall time series from stochastic point process rainfall models, such as Bartlett–Lewis models. The present study investigates whether drought statistics are preserved when simulating rainfall with Bartlett–Lewis models. Therefore, a 105 yr 10 min rainfall time series obtained at Uccle, Belgium is used as a test case. First, drought events were identified on the basis of the Effective Drought Index (EDI), and each event was characterized by two variables, i.e. drought duration (D) and drought severity (S). As both parameters are interdependent, a multivariate distribution function, which makes use of a copula, was fitted. Based on the copula, four types of drought return periods are calculated for observed as well as simulated droughts and are used to evaluate the ability of the rainfall models to simulate drought events with the appropriate characteristics. Overall, all Bartlett–Lewis model types studied fail to preserve extreme drought statistics, which is attributed to the model structure and to the model stationarity caused by maintaining the same parameter set during the whole simulation period.

scholarly journals Effect of Climate Change on Maize Yield in the Growing Season: A Case Study of the Songliao Plain Maize Belt

Water ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 2108 ◽  
Author(s):  
Ari Guna ◽  
Jiquan Zhang ◽  
Siqin Tong ◽  
Yongbin Bao ◽  
Aru Han ◽  
...  
Keyword(s):  
Global Warming ◽  
Coupled Model ◽  
Growing Season ◽  
Maize Yield ◽  
Climate Data ◽  
Yield Data ◽  
Increasing Temperature ◽  
Songliao Plain ◽  
The Relationship

Based on the 1965–2017 climate data of 18 meteorological stations in the Songliao Plain maize belt, the Coupled Model Intercomparision Project (CMIP5) data, and the 1998–2017 maize yield data, the drought change characteristics in the study area were analyzed by using the standardized precipitation evapotranspiration index (SPEI) and the Mann–Kendall mutation test; furthermore, the relationship between meteorological factors, drought index, and maize climate yield was determined. Finally, the maize climate yields under 1.5 °C and 2.0 °C global warming scenarios were predicted. The results revealed that: (1) from 1965 to 2017, the study area experienced increasing temperature, decreasing precipitation, and intensifying drought trends; (2) the yield of the study area showed a downward trend from 1998 to 2017. Furthermore, the climate yield was negatively correlated with temperature, positively correlated with precipitation, and positively correlated with SPEI-1 and SPEI-3; and (3) under the 1.5 °C and the 2.0 °C global warming scenarios, the temperature and the precipitation increased in the maize growing season. Furthermore, under the studied global warming scenarios, the yield changes predicted by multiple regression were −7.7% and −15.9%, respectively, and the yield changes predicted by one-variable regression were −12.2% and −21.8%, respectively.

scholarly journals The Decadal Climate Prediction Project (DCPP) contribution to CMIP6

2016 ◽  
Vol 9 (10) ◽  
pp. 3751-3777 ◽  
Author(s):  
George J. Boer ◽  
Douglas M. Smith ◽  
Christophe Cassou ◽  
Francisco Doblas-Reyes ◽  
Gokhan Danabasoglu ◽  
...  
Keyword(s):  
Decadal Variability ◽  
Coupled Model ◽  
Climate Prediction ◽  
Grand Challenge ◽  
Decadal Prediction ◽  
Climate Data ◽  
Intercomparison Project ◽  
Near Term ◽  
Decadal Climate ◽  
Decadal Climate Prediction

Abstract. The Decadal Climate Prediction Project (DCPP) is a coordinated multi-model investigation into decadal climate prediction, predictability, and variability. The DCPP makes use of past experience in simulating and predicting decadal variability and forced climate change gained from the fifth Coupled Model Intercomparison Project (CMIP5) and elsewhere. It builds on recent improvements in models, in the reanalysis of climate data, in methods of initialization and ensemble generation, and in data treatment and analysis to propose an extended comprehensive decadal prediction investigation as a contribution to CMIP6 (Eyring et al., 2016) and to the WCRP Grand Challenge on Near Term Climate Prediction (Kushnir et al., 2016). The DCPP consists of three components. Component A comprises the production and analysis of an extensive archive of retrospective forecasts to be used to assess and understand historical decadal prediction skill, as a basis for improvements in all aspects of end-to-end decadal prediction, and as a basis for forecasting on annual to decadal timescales. Component B undertakes ongoing production, analysis and dissemination of experimental quasi-real-time multi-model forecasts as a basis for potential operational forecast production. Component C involves the organization and coordination of case studies of particular climate shifts and variations, both natural and naturally forced (e.g. the “hiatus”, volcanoes), including the study of the mechanisms that determine these behaviours. Groups are invited to participate in as many or as few of the components of the DCPP, each of which are separately prioritized, as are of interest to them.The Decadal Climate Prediction Project addresses a range of scientific issues involving the ability of the climate system to be predicted on annual to decadal timescales, the skill that is currently and potentially available, the mechanisms involved in long timescale variability, and the production of forecasts of benefit to both science and society.

scholarly journals Characterizing drought in terms of changes in the precipitation–runoff relationship: a case study of the Loess Plateau, China

2018 ◽  
Vol 22 (3) ◽  
pp. 1749-1766 ◽  
Author(s):  
Yuan Zhang ◽  
Xiaoming Feng ◽  
Xiaofeng Wang ◽  
Bojie Fu
Keyword(s):  
Loess Plateau ◽  
Mean Annual Precipitation ◽  
Water Yield ◽  
Drought Severity ◽  
The Loess Plateau ◽  
Drought Duration ◽  
Continuous Precipitation ◽  
Area Index ◽  
Limited Region

Abstract. The frequency and intensity of drought are increasing dramatically with global warming. However, few studies have characterized drought in terms of its impacts on ecosystem services, the mechanisms through which ecosystems support life. As a result, little is known about the implications of increased drought for resource management. This case study characterizes drought by linking climate anomalies with changes in the precipitation–runoff relationship (PRR) on the Loess Plateau of China, a water-limited region where ongoing revegetation makes drought a major concern. We analyzed drought events with drought durations ≥ 5 years and mean annual precipitation anomaly (PA) values ≤ −5 % during drought periods. The results show that continuous precipitation shifts are able to change the water balance of watersheds in water-limited areas, and multi-year drought events cause the PRR to change with a significantly decreasing trend (p < 0.05) compared to other historical records. For the Loess Plateau as a whole, the average runoff ratio decreased from 10 to 6.8  % during 1991–1999. The joint probability and return period gradually increase with increasing of drought duration and severity. The ecosystem service of water yield is easily affected by drought events with durations equal to or greater than 6 years and drought severity values equal to or greater than 0.55 (precipitation ≤ 212 mm). At the same time, multi-year drought events also lead to significant changes in the leaf area index (LAI). Such studies are essential for ecosystem management in water-limited areas.

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