The future of Antarctica's surface winds simulated by a high-resolution global climate model: 2. Drivers of 21st century changes

Abstract. The Climate SPHINX (Stochastic Physics HIgh resolutioN eXperiments) project is a comprehensive set of ensemble simulations aimed at evaluating the sensitivity of present and future climate to model resolution and stochastic parameterisation. The EC-Earth Earth system model is used to explore the impact of stochastic physics in a large ensemble of 30-year climate integrations at five different atmospheric horizontal resolutions (from 125 up to 16 km). The project includes more than 120 simulations in both a historical scenario (1979–2008) and a climate change projection (2039–2068), together with coupled transient runs (1850–2100). A total of 20.4 million core hours have been used, made available from a single year grant from PRACE (the Partnership for Advanced Computing in Europe), and close to 1.5 PB of output data have been produced on SuperMUC IBM Petascale System at the Leibniz Supercomputing Centre (LRZ) in Garching, Germany. About 140 TB of post-processed data are stored on the CINECA supercomputing centre archives and are freely accessible to the community thanks to an EUDAT data pilot project. This paper presents the technical and scientific set-up of the experiments, including the details on the forcing used for the simulations performed, defining the SPHINX v1.0 protocol. In addition, an overview of preliminary results is given. An improvement in the simulation of Euro-Atlantic atmospheric blocking following resolution increase is observed. It is also shown that including stochastic parameterisation in the low-resolution runs helps to improve some aspects of the tropical climate – specifically the Madden–Julian Oscillation and the tropical rainfall variability. These findings show the importance of representing the impact of small-scale processes on the large-scale climate variability either explicitly (with high-resolution simulations) or stochastically (in low-resolution simulations).

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The role of serial European windstorm clustering for extreme seasonal losses as determined from multi-centennial simulations of high-resolution global climate model data

Natural Hazards and Earth System Science ◽

10.5194/nhess-18-2991-2018 ◽

2018 ◽

Vol 18 (11) ◽

pp. 2991-3006 ◽

Cited By ~ 4

Author(s):

Matthew D. K. Priestley ◽

Helen F. Dacre ◽

Len C. Shaffrey ◽

Kevin I. Hodges ◽

Joaquim G. Pinto

Keyword(s):

High Resolution ◽

Return Period ◽

Large Scale ◽

Climate Model ◽

Global Climate ◽

Global Climate Model ◽

Geographic Region ◽

Near Surface ◽

Short Period

Abstract. Extratropical cyclones are the most damaging natural hazard to affect western Europe. Serial clustering occurs when many intense cyclones affect one specific geographic region in a short period of time which can potentially lead to very large seasonal losses. Previous studies have shown that intense cyclones may be more likely to cluster than less intense cyclones. We revisit this topic using a high-resolution climate model with the aim to determine how important clustering is for windstorm-related losses. The role of windstorm clustering is investigated using a quantifiable metric (storm severity index, SSI) that is based on near-surface meteorological variables (10 m wind speed) and is a good proxy for losses. The SSI is used to convert a wind footprint into losses for individual windstorms or seasons. 918 years of a present-day ensemble of coupled climate model simulations from the High-Resolution Global Environment Model (HiGEM) are compared to ERA-Interim reanalysis. HiGEM is able to successfully reproduce the wintertime North Atlantic/European circulation, and represent the large-scale circulation associated with the serial clustering of European windstorms. We use two measures to identify any changes in the contribution of clustering to the seasonal windstorm loss as a function of return period. Above a return period of 3 years, the accumulated seasonal loss from HiGEM is up to 20 % larger than the accumulated seasonal loss from a set of random resamples of the HiGEM data. Seasonal losses are increased by 10 %–20 % relative to randomized seasonal losses at a return period of 200 years. The contribution of the single largest event in a season to the accumulated seasonal loss does not change with return period, generally ranging between 25 % and 50 %. Given the realistic dynamical representation of cyclone clustering in HiGEM, and comparable statistics to ERA-Interim, we conclude that our estimation of clustering and its dependence on the return period will be useful for informing the development of risk models for European windstorms, particularly for longer return periods.

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Analysis and Projection of Flood Hazards over China

Water ◽

10.3390/w11051022 ◽

2019 ◽

Vol 11 (5) ◽

pp. 1022 ◽

Cited By ~ 8

Author(s):

Yulian Liang ◽

Yongli Wang ◽

Yinjun Zhao ◽

Yuan Lu ◽

Xiaoying Liu

Keyword(s):

21St Century ◽

Flood Control ◽

Climate Model ◽

Southern China ◽

Flood Hazard ◽

Global Climate ◽

Global Climate Model ◽

Flood Hazards ◽

Rcp Scenarios ◽

Meteorological Observations

Floods have been experienced with greater frequency and more severity under global climate change. To understand the flood hazard and its variation in the future, the current and future flood hazards in the 21st century in China are discussed. Floods and their trends are assessed using the accumulation precipitation during heavy rainfall process (AP_HRP), which are calculated based on historical meteorological observations and the outputs of a global climate model (GCM) under three Representative Concentration Pathway (RCP) scenarios. The flood-causing HRPs counted by the flood-causing critical precipitation (the 60% fractile of AP_HRP) capture more than 70% of historical flood events. The projection results indicate that the flood hazards could increase under RCP4.5 and RCP8.5 and increase slightly under RCP2.6 during the 21st century (2011–2099). The spatial characteristics of flood hazards and their increasing trends under the three RCPs are similar in most areas of China. More floods could occur in southern China, including Guangdong, Hainan, Guangxi and Fujian provinces, which could become more serious in southeastern China and the northern Yunnan province. Construction of water conservancy projects, reservoir dredging, improvement of drainage and irrigation equipment and enhancement of flood control and storage capacity can mitigate the impacts of floods and waterlogging on agriculture.

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Statistical Projection of the North Atlantic Storm Tracks

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-17-0348.1 ◽

2019 ◽

Vol 58 (7) ◽

pp. 1509-1522 ◽

Cited By ~ 3

Author(s):

Kajsa M. Parding ◽

Rasmus Benestad ◽

Abdelkader Mezghani ◽

Helene B. Erlandsen

Keyword(s):

North Atlantic ◽

Geopotential Height ◽

Climate Model ◽

Global Climate ◽

Global Climate Model ◽

Storm Tracks ◽

Cmip5 Models ◽

The North ◽

The Future ◽

The North Atlantic

AbstractA method for empirical–statistical downscaling was adapted to project seasonal cyclone density over the North Atlantic Ocean. To this aim, the seasonal mean cyclone density was derived from instantaneous values of the 6-h mean sea level pressure (SLP) reanalysis fields. The cyclone density was then combined with seasonal mean reanalysis and global climate model projections of SLP or 500-hPa geopotential height to obtain future projections of the North Atlantic storm tracks. The empirical–statistical approach is computationally efficient because it makes use of seasonally aggregated cyclone statistics and allows the future cyclone density to be estimated from the full ensemble of available CMIP5 models rather than from a smaller subset. However, the projected cyclone density in the future differs considerably depending on the choice of predictor, SLP, or 500-hPa geopotential height. This discrepancy suggests that the relationship between the cyclone density and SLP, 500-hPa geopotential height, or both is nonstationary; that is, that the statistical model depends on the calibration period. A stationarity test based on 6-hourly HadGEM2-ES data indicated that the 500-hPa geopotential height was not a robust predictor of cyclone density.

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Quantification of the Expected Changes in Annual Maximum Daily Precipitation Quantiles under Climate Change in the Iberian Peninsula

Proceedings ◽

10.3390/ecws-3-05819 ◽

2018 ◽

Vol 7 (1) ◽

pp. 23 ◽

Cited By ~ 1

Author(s):

Carlos Garijo ◽

Luis Mediero

Keyword(s):

Climate Change ◽

Iberian Peninsula ◽

Climate Models ◽

Climate Model ◽

Global Climate ◽

Global Climate Model ◽

Regional Climate ◽

Control Period ◽

Regional Climate Models ◽

The Future

Climate model projections can be used to assess the expected behaviour of extreme precipitations in the future due to climate change. The European part of the Coordinated Regional Climate Downscalling Experiment (EURO-CORDEX) provides precipitation projections for the future under various representative concentration pathways (RCPs) through regionalised Global Climate Model (GCM) outputs by a set of Regional Climate Models (RCMs). In this work, 12 combinations of GCM and RCM under two scenarios (RCP 4.5 and RCP 8.5) supplied by the EURO-CORDEX are analysed for the Iberian Peninsula. Precipitation quantiles for a set of probabilities of non-exceedance are estimated by using the Generalized Extreme Value (GEV) distribution and L-moments. Precipitation quantiles expected in the future are compared with the precipitation quantiles in the control period for each climate model. An approach based on Monte Carlo simulations is developed in order to assess the uncertainty from the climate model projections. Expected changes in the future are compared with the sampling uncertainty in the control period. Thus, statistically significant changes are identified. The higher the significance threshold, the fewer cells with significant changes are identified. Consequently, a set of maps are obtained in order to assist the decision-making process in subsequent climate change studies.

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Future projections of Indian Summer Monsoon under multiple RCPs using a high resolution global climate model multiforcing ensemble simulations

Climate Dynamics ◽

10.1007/s00382-019-05059-7 ◽

2019 ◽

Vol 54 (3-4) ◽

pp. 1315-1328 ◽

Cited By ~ 2

Author(s):

Stella Jes Varghese ◽

Sajani Surendran ◽

Kavirajan Rajendran ◽

Akio Kitoh

Keyword(s):

High Resolution ◽

Summer Monsoon ◽

Indian Summer Monsoon ◽

Climate Model ◽

Global Climate ◽

Global Climate Model ◽

Future Projections ◽

Ensemble Simulations

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Statistical–Dynamical Predictions of Seasonal North Atlantic Hurricane Activity

Monthly Weather Review ◽

10.1175/2010mwr3499.1 ◽

2011 ◽

Vol 139 (4) ◽

pp. 1070-1082 ◽

Cited By ~ 97

Author(s):

Gabriel A. Vecchi ◽

Ming Zhao ◽

Hui Wang ◽

Gabriele Villarini ◽

Anthony Rosati ◽

...

Keyword(s):

High Resolution ◽

North Atlantic ◽

Climate Model ◽

Global Climate ◽

Global Climate Model ◽

Atlantic Hurricane ◽

Uncertainty Estimates ◽

Hurricane Season ◽

Hurricane Activity ◽

Hurricane Forecasting

Abstract Skillfully predicting North Atlantic hurricane activity months in advance is of potential societal significance and a useful test of our understanding of the factors controlling hurricane activity. In this paper, a statistical–dynamical hurricane forecasting system, based on a statistical hurricane model, with explicit uncertainty estimates, and built from a suite of high-resolution global atmospheric dynamical model integrations spanning a broad range of climate states is described. The statistical model uses two climate predictors: the sea surface temperature (SST) in the tropical North Atlantic and SST averaged over the global tropics. The choice of predictors is motivated by physical considerations, as well as the results of high-resolution hurricane modeling and statistical modeling of the observed record. The statistical hurricane model is applied to a suite of initialized dynamical global climate model forecasts of SST to predict North Atlantic hurricane frequency, which peaks during the August–October season, from different starting dates. Retrospective forecasts of the 1982–2009 period indicate that skillful predictions can be made from as early as November of the previous year; that is, skillful forecasts for the coming North Atlantic hurricane season could be made as the current one is closing. Based on forecasts initialized between November 2009 and March 2010, the model system predicts that the upcoming 2010 North Atlantic hurricane season will likely be more active than the 1982–2009 climatology, with the forecasts initialized in March 2010 predicting an expected hurricane count of eight and a 50% probability of counts between six (the 1966–2009 median) and nine.

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Projected changes in medicanes in the HadGEM3 N512 high-resolution global climate model

Climate Dynamics ◽

10.1007/s00382-015-2941-2 ◽

2015 ◽

Vol 47 (5-6) ◽

pp. 1913-1924 ◽

Cited By ~ 15

Author(s):

M. Tous ◽

G. Zappa ◽

R. Romero ◽

L. Shaffrey ◽

P. L. Vidale

Keyword(s):

High Resolution ◽

Climate Model ◽

Global Climate ◽

Global Climate Model ◽

Projected Changes

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Changes in Climate Extremes over North Thailand, 1960–2099

Journal of Climatology ◽

10.1155/2016/4289454 ◽

2016 ◽

Vol 2016 ◽

pp. 1-18 ◽

Cited By ~ 14

Author(s):

Mohammad Badrul Masud ◽

Peeyush Soni ◽

Sangam Shrestha ◽

Nitin K. Tripathi

Keyword(s):

Climate Model ◽

Global Climate ◽

Global Climate Model ◽

Annual Rainfall ◽

Trend Test ◽

Daily Maximum ◽

Climate Extreme ◽

The Future ◽

North Thailand ◽

Extreme Indices

This study analyzes 24 climate extreme indices over North Thailand using observed data for daily maximum and minimum temperatures and total daily rainfall for the 1960–2010 period, and HadCM3 Global Climate Model (GCM) and PRECIS Regional Climate Model simulated data for the 1960–2100 period. A statistical downscaling tool is employed to downscale GCM outputs. Variations in and trends of historical and future climates are identified using the nonparametric Mann-Kendall trend test and Sen’s slope. Temperature extreme indices showed a significant rising trend during the observed period and are expected to increase significantly with an increase in summer days and tropical nights in the future. A notable decline in the number of cool days and nights is also expected in the study area while the number of warm days and nights is expected to increase. There was an insignificant decrease in total annual rainfall, number of days with rainfall more than 10 and 20 mm. However, the annual rainfall is projected to increase by 9.65% in the future 2011–2099 period compared to the observed 1960–2010 period.

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