Changing intensity and seasonality of wet extremes over Europe 

2021 ◽  
Author(s):  
Amal John ◽  
Hervé Douville ◽  
Pascal Yiou
Keyword(s):  
Global Warming ◽  
Daily Precipitation ◽  
Surface Air Temperature ◽  
Value Theory ◽  
Precipitation Extremes ◽  
Annual Maximum ◽  
Physical Mechanisms ◽  
Model Distribution ◽  
Future Global Warming ◽  
Global Mean

<p>Daily precipitation extremes are projected to intensify with global warming. Here the focus is on how extreme precipitation scales with the changing global mean surface air temperature (GSAT) and how much their inherent seasonality will change, using historical and SSP5-8.5 scenario simulations from 18 CMIP6 models for different sub-domains over Europe. With strong future global warming, the annual maximum precipitation (RX1DAY) is found to occur later in the year, although this shift is model-dependent and hardly significant in the multi-model distribution. Using generalized extreme value theory also provides evidence for the intensification of wet extremes in the future. In addition, we use monthly model outputs to decompose changes in RX1DAY occurring at the peak of the extreme season into several contributions, which gives insights into the underlying physical mechanisms that control the response of precipitation extremes and their inter-model spread.</p>

scholarly journals Global climate shift in 1970s causes a significant worldwide increase in precipitation extremes

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Subharthi Sarkar ◽  
Rajib Maity
Keyword(s):  
Daily Precipitation ◽  
Global Climate ◽  
Precipitation Extremes ◽  
Annual Maximum ◽  
Arctic Amplification ◽  
Land Area ◽  
Global Land ◽  
Spatial Redistribution ◽  
Heat And Moisture Transport ◽  
Heat And Moisture

AbstractThe shift in climate regimes around 1970s caused an overall enhancement of precipitation extremes across the globe with a specific spatial distribution pattern. We used gridded observational-reanalysis precipitation dataset and two important extreme precipitation measures, namely Annual Maximum Daily Precipitation (AMDP) and Probable Maximum Precipitation (PMP). AMDP is reported to increase for almost two-third of the global land area. The variability of AMDP is found to increase more than its mean that eventually results in increased PMP almost worldwide, less near equator and maximum around mid-latitudes. Continent-wise, such increase in AMDP and PMP is true for all continents except some parts of Africa. The zone-wise analysis (dividing the globe into nine precipitation zones) reveals that zones of ‘moderate precipitation’ and ‘moderate seasonality’ exhibit the maximum increases in PMP. Recent increased in pole-ward heat and moisture transport as a result of Arctic Amplification may be associated with such spatial redistribution of precipitation extremes in the northern hemisphere.

scholarly journals Global trends in extreme precipitation: climate models versus observations

2015 ◽  
Vol 19 (2) ◽  
pp. 877-891 ◽  
Author(s):  
B. Asadieh ◽  
N. Y. Krakauer
Keyword(s):  
Global Warming ◽  
Extreme Precipitation ◽  
Climate Models ◽  
Daily Precipitation ◽  
Average Rate ◽  
Global Scale ◽  
Global Climate Models ◽  
Cmip5 Models ◽  
Annual Maximum ◽  
Rate Of Increase

Abstract. Precipitation events are expected to become substantially more intense under global warming, but few global comparisons of observations and climate model simulations are available to constrain predictions of future changes in precipitation extremes. We present a systematic global-scale comparison of changes in historical (1901–2010) annual-maximum daily precipitation between station observations (compiled in HadEX2) and the suite of global climate models contributing to the fifth phase of the Coupled Model Intercomparison Project (CMIP5). We use both parametric and non-parametric methods to quantify the strength of trends in extreme precipitation in observations and models, taking care to sample them spatially and temporally in comparable ways. We find that both observations and models show generally increasing trends in extreme precipitation since 1901, with the largest changes in the deep tropics. Annual-maximum daily precipitation (Rx1day) has increased faster in the observations than in most of the CMIP5 models. On a global scale, the observational annual-maximum daily precipitation has increased by an average of 5.73 mm over the last 110 years, or 8.5% in relative terms. This corresponds to an increase of 10% K−1 in global warming since 1901, which is larger than the average of climate models, with 8.3% K−1. The average rate of increase in extreme precipitation per K of warming in both models and observations is higher than the rate of increase in atmospheric water vapor content per K of warming expected from the Clausius–Clapeyron equation. We expect our findings to help inform assessments of precipitation-related hazards such as flooding, droughts and storms.

Probing the Fast and Slow Components of Global Warming by Returning Abruptly to Preindustrial Forcing

2010 ◽  
Vol 23 (9) ◽  
pp. 2418-2427 ◽  
Author(s):  
Isaac M. Held ◽  
Michael Winton ◽  
Ken Takahashi ◽  
Thomas Delworth ◽  
Fanrong Zeng ◽  
...  
Keyword(s):  
Global Warming ◽  
Radiative Forcing ◽  
Climate Model ◽  
Slow Component ◽  
Surface Air Temperature ◽  
Special Report ◽  
Near Surface ◽  
Ensemble Mean ◽  
Emissions Scenarios ◽  
Global Mean

Abstract The fast and slow components of global warming in a comprehensive climate model are isolated by examining the response to an instantaneous return to preindustrial forcing. The response is characterized by an initial fast exponential decay with an e-folding time smaller than 5 yr, leaving behind a remnant that evolves more slowly. The slow component is estimated to be small at present, as measured by the global mean near-surface air temperature, and, in the model examined, grows to 0.4°C by 2100 in the A1B scenario from the Special Report on Emissions Scenarios (SRES), and then to 1.4°C by 2300 if one holds radiative forcing fixed after 2100. The dominance of the fast component at present is supported by examining the response to an instantaneous doubling of CO2 and by the excellent fit to the model’s ensemble mean twentieth-century evolution with a simple one-box model with no long times scales.

scholarly journals A less cloudy picture of the inter-model spread in future global warming projections

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Xiaoming Hu ◽  
Hanjie Fan ◽  
Ming Cai ◽  
Sergio A. Sejas ◽  
Patrick Taylor ◽  
...  
Keyword(s):  
Global Warming ◽  
Water Vapor ◽  
Polar Regions ◽  
Dependent Manner ◽  
Mean Values ◽  
Spatial Coupling ◽  
Regional Impacts ◽  
Future Global Warming ◽  
Nature And Society ◽  
Global Mean

Abstract Model warming projections, forced by increasing greenhouse gases, have a large inter-model spread in both their geographical warming patterns and global mean values. The inter-model warming pattern spread (WPS) limits our ability to foresee the severity of regional impacts on nature and society. This paper focuses on uncovering the feedbacks responsible for the WPS. Here, we identify two dominant WPS modes whose global mean values also explain 98.7% of the global warming spread (GWS). We show that the ice-albedo feedback spread explains uncertainties in polar regions while the water vapor feedback spread explains uncertainties elsewhere. Other processes, including the cloud feedback, contribute less to the WPS as their spreads tend to cancel each other out in a model-dependent manner. Our findings suggest that the WPS and GWS could be significantly reduced by narrowing the inter-model spreads of ice-albedo and water vapor feedbacks, and better understanding the spatial coupling between feedbacks.

scholarly journals Global trends in extreme precipitation: climate models vs. observations

2014 ◽  
Vol 11 (10) ◽  
pp. 11369-11393 ◽  
Author(s):  
B. Asadieh ◽  
N. Y. Krakauer
Keyword(s):  
Global Warming ◽  
Extreme Precipitation ◽  
Climate Models ◽  
Climate Model ◽  
Daily Precipitation ◽  
Average Rate ◽  
Global Climate Models ◽  
Cmip5 Models ◽  
Annual Maximum ◽  
Rate Of Increase

Abstract. Precipitation events are expected to become substantially more intense under global warming, but few global comparisons of observations and climate model simulations are available to constrain predictions of future changes in precipitation extremes. We present a systematic global-scale comparison of changes in historical (1901–2010) annual-maximum daily precipitation between station observations (compiled in HadEX2) and the suite of global climate models contributing to the fifth phase of the Coupled Model Inter-comparison Project (CMIP5). We use both parametric and non-parametric methods to quantify the strength of trends in extreme precipitation in observations and models, taking care to spatially and temporally sample them in comparable ways. We find that both observations and models show generally increasing trends in extreme precipitation since 1901 with largest changes in deep tropics, although annual-maximum daily precipitation has increased faster in the observations than in most of the CMIP5 models. Global average of observational annual-maximum daily precipitation has increased about 5.73 mm over the last 110 years or 8.5% in relative terms and has increased by approximately 10% per K of global warming since 1901, which is larger than the average of climate models with 8.3% K−1. The average rate of increase in extreme precipitation per K of warming in models and observations is higher than the rate of increase in atmospheric water vapor content per K of warming expected from the Clausius–Clapeyron equation. We expect our findings to help inform assessments of precipitation-related hazards such as flooding, droughts and storms.

scholarly journals An updated evaluation of the global mean land surface air temperature and surface temperature trends based on CLSAT and CMST

2021 ◽  
Vol 56 (1-2) ◽  
pp. 635-650 ◽  
Author(s):  
Qingxiang Li ◽  
Wenbin Sun ◽  
Xiang Yun ◽  
Boyin Huang ◽  
Wenjie Dong ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Air Temperature ◽  
Land Surface ◽  
Surface Air Temperature ◽  
Temperature Trends ◽  
Global Mean

Gaussian Process emulation of multi-model ice sheet and glacier projections

2021 ◽  
Author(s):  
Tamsin Edwards ◽  
Keyword(s):  
Gaussian Process ◽  
Sea Level Rise ◽  
Sea Level ◽  
Climate Models ◽  
Ice Sheet ◽  
Surface Air Temperature ◽  
Model Parameters ◽  
Gaussian Process Emulation ◽  
Model Ensembles ◽  
Global Mean

<p><strong>The land ice contribution to global mean sea level rise has not yet been predicted with ice sheet and glacier models for the latest set of socio-economic scenarios (SSPs), nor with coordinated exploration of uncertainties arising from the various computer models involved. Two recent international projects (ISMIP6 and GlacierMIP) generated a large suite of projections using multiple models, but mostly used previous generation scenarios and climate models, and could not fully explore known uncertainties. </strong></p><p><strong>Here we estimate probability distributions for these projections for the SSPs using Gaussian Process emulation of the ice sheet and glacier model ensembles. We model the sea level contribution as a function of global mean surface air temperature forcing and (for the ice sheets) model parameters, with the 'nugget' allowing for multi-model structural uncertainty. Approximate independence of ice sheet and glacier models is assumed, because a given model responds very differently under different setups (such as initialisation). </strong></p><p><strong>We find that limiting global warming to 1.5</strong>°<strong>C </strong><strong>would halve the land ice contribution to 21<sup>st</sup> century </strong><strong>sea level rise</strong><strong>, relative to current emissions pledges: t</strong><strong>he median decreases from 25 to 13 cm sea level equivalent (SLE) by 2100. However, the Antarctic contribution does not show a clear response to emissions scenario, due to competing processes of increasing ice loss and snowfall accumulation in a warming climate. </strong></p><p><strong>However, under risk-averse (pessimistic) assumptions for climate and Antarctic ice sheet model selection and ice sheet model parameter values, Antarctic ice loss could be five times higher, increasing the median land ice contribution to 42 cm SLE under current policies and pledges, with the 95<sup>th</sup> percentile exceeding half a metre even under 1.5</strong>°<strong>C warming. </strong></p><p><strong>Gaussian Process emulation can therefore be a powerful tool for estimating probability density functions from multi-model ensembles and testing the sensitivity of the results to assumptions.</strong></p>

Data-model Comparison for the mid-Pliocene Warm Period

2021 ◽  
Author(s):  
Julia Tindall ◽  
Alan Haywood ◽  
Ulrich Salzmann ◽  
Aisling Dolan
Keyword(s):  
Data Model ◽  
Model Comparison ◽  
Surface Air Temperature ◽  
High Latitudes ◽  
Orbital Forcing ◽  
Local Site ◽  
Modern Analogue ◽  
Intercomparison Project ◽  
Global Mean ◽  
Good Agreement

<p>Modelling results from PlioMIP2 (the Pliocene Model Intercomparison Project Phase 2) focussing on MIS KM5c; ~3.205Ma, suggest that global mean surface air temperature was 1.7 – 5.2 °C higher than the preindustrial.  This warming was amplified at the poles and over land.  The results are in reasonable agreement with paleodata over the ocean.   </p><p>Over the land the situation is more complicated.  Model and data are in very good agreement at lower latitudes, however at high latitudes an initial data-model comparison shows much warmer mPWP temperatures from data than from models.   </p><p>Here we consider possible reasons for this data-model discord at high latitudes.  These include uncertainties in model boundary conditions (such as CO<sub>2 </sub>and orbital forcing), and whether there are local site-specific conditions which need to be accounted for.  We also show that the seasonal cycle in mPWP temperatures at these high latitude sites has no modern analogue.  This could lead to inaccuracies when comparing model derived mean annual temperatures with quantitative climatic estimates from palaeobotanical data using Nearest Living Relative methods.</p>

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