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

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

Journal of Geophysical Research Atmospheres ◽

10.1002/2013jd020848 ◽

2014 ◽

Vol 119 (12) ◽

pp. 7160-7178 ◽

Cited By ~ 6

Author(s):

R. Bintanja ◽

C. Severijns ◽

R. Haarsma ◽

W. Hazeleger

Keyword(s):

High Resolution ◽

21St Century ◽

Climate Model ◽

Global Climate ◽

Global Climate Model ◽

Surface Winds ◽

The Future

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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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Distributions of Tropical Precipitation Cluster Power and Their Changes under Global Warming. Part I: Observational Baseline and Comparison to a High-Resolution Atmospheric Model

Journal of Climate ◽

10.1175/jcli-d-16-0683.1 ◽

2017 ◽

Vol 30 (20) ◽

pp. 8033-8044 ◽

Cited By ~ 6

Author(s):

Kevin M. Quinn ◽

J. David Neelin

Keyword(s):

Global Warming ◽

High Resolution ◽

Rain Rate ◽

Climate Model ◽

Global Climate ◽

Global Climate Model ◽

Aggregate Size ◽

Atmospheric Model ◽

Rain Rates ◽

High Cluster

Abstract The total amount of precipitation integrated across a precipitation feature (contiguous precipitating grid cells exceeding a minimum rain rate) is a useful measure of the aggregate size of the disturbance, expressed as the rate of water mass lost or latent heat released (i.e., the power of the disturbance). The probability distribution of cluster power is examined over the tropics using Tropical Rainfall Measuring Mission (TRMM) 3B42 satellite-retrieved rain rates and global climate model output. Observed distributions are scale-free from the smallest clusters up to a cutoff scale at high cluster power, after which the probability drops rapidly. After establishing an observational baseline, precipitation from the High Resolution Atmospheric Model (HiRAM) at two horizontal grid spacings (roughly 0.5° and 0.25°) is compared. When low rain rates are excluded by choosing a minimum rain-rate threshold in defining clusters, the model accurately reproduces observed cluster power statistics at both resolutions. Middle and end-of-century cluster power distributions are investigated in HiRAM in simulations with prescribed sea surface temperatures and greenhouse gas concentrations from a “business as usual” global warming scenario. The probability of high cluster power events increases strongly by end of century, exceeding a factor of 10 for the highest power events for which statistics can be computed. Clausius–Clapeyron scaling accounts for only a fraction of the increased probability of high cluster power events.

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On the Seasonal Forecasting of Regional Tropical Cyclone Activity

Journal of Climate ◽

10.1175/jcli-d-14-00158.1 ◽

2014 ◽

Vol 27 (21) ◽

pp. 7994-8016 ◽

Cited By ~ 231

Author(s):

G. A. Vecchi ◽

T. Delworth ◽

R. Gudgel ◽

S. Kapnick ◽

A. Rosati ◽

...

Keyword(s):

High Resolution ◽

Climate Model ◽

Global Climate ◽

Spatial Scales ◽

Global Climate Model ◽

Regional Climate ◽

Weather Extremes ◽

Moderate Resolution ◽

Systematic Biases ◽

Constrained Conditions

Abstract Tropical cyclones (TCs) are a hazard to life and property and a prominent element of the global climate system; therefore, understanding and predicting TC location, intensity, and frequency is of both societal and scientific significance. Methodologies exist to predict basinwide, seasonally aggregated TC activity months, seasons, and even years in advance. It is shown that a newly developed high-resolution global climate model can produce skillful forecasts of seasonal TC activity on spatial scales finer than basinwide, from months and seasons in advance of the TC season. The climate model used here is targeted at predicting regional climate and the statistics of weather extremes on seasonal to decadal time scales, and comprises high-resolution (50 km × 50 km) atmosphere and land components as well as more moderate-resolution (~100 km) sea ice and ocean components. The simulation of TC climatology and interannual variations in this climate model is substantially improved by correcting systematic ocean biases through “flux adjustment.” A suite of 12-month duration retrospective forecasts is performed over the 1981–2012 period, after initializing the climate model to observationally constrained conditions at the start of each forecast period, using both the standard and flux-adjusted versions of the model. The standard and flux-adjusted forecasts exhibit equivalent skill at predicting Northern Hemisphere TC season sea surface temperature, but the flux-adjusted model exhibits substantially improved basinwide and regional TC activity forecasts, highlighting the role of systematic biases in limiting the quality of TC forecasts. These results suggest that dynamical forecasts of seasonally aggregated regional TC activity months in advance are feasible.

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Simulations of Atmospheric Rivers in GFDL New Generation High Resolution Global Climate Model

10.5194/egusphere-egu2020-1596 ◽

2020 ◽

Author(s):

Ming Zhao

Keyword(s):

Global Warming ◽

High Resolution ◽

Climate Model ◽

Global Climate ◽

Global Climate Model ◽

Climate Extremes ◽

The Arctic ◽

Atmospheric Rivers ◽

Weather And Climate ◽

New Generation

<p>Atmospheric rivers (ARs) are narrow, elongated, synoptic jets of water vapor that play important roles in the global water cycle and regional weather and climate extremes. Accurate climate projections of high impact global severe flood and drought events hinge on the climate models' ability to simulate and predict the AR phenomenon. This presentation will provide a systematic evaluation of the AR statistics and characteristics simulated by the GFDL new generation high resolution global climate model participating in the CMIP6 High Resolution Model Intercomparison Project (HiResMIP). The analyses include the historical period (1950-2014) compared against the ERA-Interim reanalysis results as well as future projections under global warming scenarios. The AR characteristics such as the spatial distribution, frequency, and intensity are explored in conjunction with large-scale circulation patterns such as the El Ni&#241;o&#8211;Southern Oscillation, the Arctic Oscillation, and the Pacific-North-American teleconnections pattern. Potential changes in AR characteristics with global warming scenarios and their implications to weather and climate extremes will be discussed.</p>

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