scholarly journals RESEARCH ON THE DIFFICULTY IN SEASONAL PREDICTION OF EXTREME PRECIPITATION EVENTS IN PAKISTAN, FOCUSING ON THE ANOMALY OF GENERAL CIRCULATION

2014 ◽  
Vol 70 (4) ◽  
pp. I_301-I_306
Author(s):  
Masashi MINAMIDE ◽  
Toshio KOIKE
Keyword(s):  
Extreme Precipitation ◽  
General Circulation ◽  
Seasonal Prediction ◽  
Extreme Precipitation Events ◽  
Precipitation Events

Predictability of precipitation extremes over the Mediterranean

2020 ◽  
Author(s):  
Ignazio Giuntoli ◽  
Federico Fabiano ◽  
Susanna Corti
Keyword(s):  
Extreme Precipitation ◽  
Winter Season ◽  
Seasonal Prediction ◽  
Reanalysis Data ◽  
Economic Activities ◽  
Weather Regimes ◽  
Extreme Precipitation Events ◽  
The Mediterranean ◽  
Prediction Systems ◽  
Precipitation Events

<p>Intense precipitations events are associated with impacts like damages to infrastructures, economic activities, agricultural crops, power production and society in general. The ability to predict extreme precipitation events months in advance is therefore of great value in densely populated areas like the Mediterranean and may be achieved using seasonal prediction systems like the Copernicus Climate Change Services (C3S) suite of models. Using weather regimes (WRs) from 500 hPa geopotential heights over the Mediterranean the two main objectives of this study are: first to identify how these regimes are linked to extreme precipitation events over the region using reanalysis data; and second to assess the ability of the C3S models in reproducing/predicting these extreme events. We identify four weather regimes for the winter season (DJF) describing the atmospheric circulation in the Mediterranean using the 1993-2016 period as reference, i.e. maximum availability of C3S hindcasts. We thus provide an assessment of the models’s ability in predicting extreme precipitation over the Mediterranean having quantified how daily precipitation anomalies are associated to each WR.</p>

Global Occurrences of Extreme Precipitation and the Madden–Julian Oscillation: Observations and Predictability

2004 ◽  
Vol 17 (23) ◽  
pp. 4575-4589 ◽  
Author(s):  
Charles Jones ◽  
Duane E. Waliser ◽  
K. M. Lau ◽  
W. Stern
Keyword(s):  
Extreme Precipitation ◽  
General Circulation ◽  
Circulation Model ◽  
Global Scale ◽  
Madden Julian Oscillation ◽  
Potential Predictability ◽  
The North ◽  
Extreme Precipitation Events ◽  
Realistic Representation ◽  
Precipitation Events

Abstract This study investigates 1) the eastward propagation of the Madden–Julian oscillation (MJO) and global occurrences of extreme precipitation, 2) the degree to which a general circulation model with a relatively realistic representation of the MJO simulates its influence on extremes, and 3) a possible modulation of the MJO on potential predictability of extreme precipitation events. The observational analysis shows increased frequency of extremes during active MJO phases in many locations. On a global scale, extreme events during active MJO periods are about 40% higher than in quiescent phases of the oscillation in locations of statistically significant signals. A 10-yr National Aeronautics and Space Administration (NASA) Goddard Laboratory for the Atmospheres (GLA) GCM simulation with fixed climatological SSTs is used to generate a control run and predictability experiments. Overall, the GLA model has a realistic representation of extremes in tropical convective regions associated with the MJO, although some shortcomings also seem to be present. The GLA model shows a robust signal in the frequency of extremes in the North Pacific and on the west coast of North America, which somewhat agrees with observational studies. The analysis of predictability experiments indicates higher success in the prediction of extremes during an active MJO than in quiescent situations. Overall, the predictability experiments indicate the mean number of correct forecasts of extremes during active MJO periods to be nearly twice the correct number of extremes during quiescent phases of the oscillation in locations of statistically significant signals.

Quantifying the added value of downscaling in extreme precipitation attribution

2020 ◽  
Author(s):  
Jonathan Eden ◽  
Bastien Dieppois
Keyword(s):  
Climate Change ◽  
Global Warming ◽  
Bias Correction ◽  
Extreme Precipitation ◽  
General Circulation ◽  
Regional Climate ◽  
Precipitation Event ◽  
Extreme Precipitation Events ◽  
Statistical Correction ◽  
Precipitation Events

<p>While there is a discernible global warming fingerprint in the increase observed daily temperature extremes, there is far greater uncertainty of the role played by anthropogenic climate change with regard to extreme precipitation. A logical progression of thought is that an increase in extreme precipitation results from the 7% increase in atmospheric moisture per 1°C global temperature increase predicted by the Clausius-Clapeyron (CC) relation.  While this is supported by observations on the global scale, rates of extreme precipitation at smaller spatial and temporal scales are influenced to a far greater extent by atmospheric circulation and vertical stability in addition to local moisture availability. Many of these processes and other features of extreme precipitation events are not sufficiently represented in general circulation model (GCM) simulations. Meanwhile, limited observational networks mean that many short-term convective events are not accurately represented in the observational data.  </p><p>Errors and biases are common to all global and regional climate models, and many users of climate information require some form of statistical correction to improve the usefulness of model output. As so-called bias correction has become commonplace in climate impact research, its development has been hastened by a sustained debate regarding model correction in general leading to techniques that merge statistical correction and downscaling, represent random variability using stochasticity and are explicitly applicable to extremes. To date, attribution of extreme precipitation has not fully utilised the tools available from recent advances in bias correction, stochastic postprocessing and statistical downscaling. In the same way that GCMs are the most important tool in making climate change projections, understanding the degree to which the nature of a particular weather event has changed due to global warming requires long-term simulations of global climate from the pre-industrial era to the present day.  The lack of a correction and/or downscaling step in almost all precipitation event attribution methodologies is therefore surprising. </p><p>Here, we present a multi-scale attribution analysis of a sample of extreme precipitation events across Europe using a blend of observation- and model-based data. Attribution information generated using the raw output of global and regional climate model ensembles will be compared to that generated using the same set of models following a statistical postprocessing and downscaling step. Our conclusions will make recommendations for the value and wider application of downscaling methodologies in attribution science.</p>

UNSEEN trends: Towards detection of changes in 100-year precipitation events over the last 35 years

2020 ◽  
Author(s):  
Timo Kelder ◽  
Malte Müller ◽  
Louise Slater ◽  
Rob Wilby ◽  
Patrik Bohlinger ◽  
...  
Keyword(s):  
Extreme Precipitation ◽  
Extreme Values ◽  
Climate Extremes ◽  
Seasonal Prediction ◽  
Extreme Value Statistics ◽  
Lead Times ◽  
Positive Trend ◽  
Sea Ice Extent ◽  
Extreme Precipitation Events ◽  
Precipitation Events

<p>Constraining the non-stationarity of climate extremes is a topical area of research that is complicated by the brevity and sparsity of observational records. For regions with available data, analyses typically focus on detecting century-long changes in the annual maxima. However, these are not necessarily impact-relevant events and hence, a potentially more pressing research challenge is the detection of changes in the 1-in-100-year event. Furthermore, recent decades have seen abrupt temperature increases and therefore detecting decadal, rather than centurial, trends may be more important. An alternative approach to the traditional analysis based on observations is to pool ensemble members from seasonal prediction systems into an UNprecedented Simulated Extreme ENsemble (UNSEEN). This method creates numerous alternative pathways of reality, thus increasing the sample size. Previous studies have shown promising results that improve design value estimates by this method. Here, we use the hindcast of the ECMWF seasonal prediction system SEAS5 and pool together four lead times and 25 ensemble members, resulting in an ensemble of 100. We assess the robustness of this method in terms of the ensemble member independence, model stability and fidelity and then use the UNSEEN ensemble to detect non-stationarities in 100-year precipitation estimates over the period 1981-2016. We justify the pooling of ensemble members and lead times through a case study of autumn 3-day extreme precipitation events across Norway and Svalbard, which shows that the ensemble members are independent and that the model is stable over lead times. Despite previously reported model biases in the sea-ice extent and the sea-surface temperature in SEAS5, validation measures indicate that the model reliably reproduces ‘visible extremes’, i.e. the seasonal maxima. Using extreme value statistics, we then compare estimated return values from observations with the UNSEEN ensemble. Results indicate that the UNSEEN approach provides significantly different extreme values for return periods above 35 years. Additionally, while it is problematic to detect trends in the 100-year values from observations, the UNSEEN approach finds a significant positive trend over Svalbard. Validating UNSEEN events and trends is a complex task, but our approach reproduces ‘visible’ extremes well, building confidence in the modeled extremes. Both Norway and Svalbard have experienced severe floods from extreme precipitation events and our UNSEEN-trends approach is the first to provide an indication of the changes in these rare events. Further application of this approach can 1) help estimating design values, especially relevant for data-scarce regions 2) detect trends in rare climate extremes, including other variables than precipitation and 3) improve our physical understanding of the non-stationarity of climate extremes, through the possible attribution of detected trends.</p>

scholarly journals Future extreme precipitation assessment in Western Norway – using a linear model approach

2009 ◽  
Vol 6 (6) ◽  
pp. 7539-7579 ◽  
Author(s):  
G. N. Caroletti ◽  
I. Barstad
Keyword(s):  
Linear Model ◽  
Extreme Precipitation ◽  
General Circulation ◽  
Control Period ◽  
Orographic Precipitation ◽  
Extreme Precipitation Events ◽  
Western Norway ◽  
Simulated Precipitation ◽  
Precipitation Events ◽  
Model Approach

Abstract. The need for local assessments of precipitation has grown in recent years due to the increase in precipitation extremes and the widespread awareness about findings of the IPCC 2003 Report on climate change. General circulation models, the most commonly used tool for climate predictions, show an increase in precipitation due to an increase in greenhouse gases (Cubash and Meehl, 2001). It is suggested that changes in extreme precipitation are easier to detect and attribute to global warming than changes in mean annual precipitation (Groisman et al., 2005). However, because of their coarse resolution, the global models are not suited to local assessments. Thus, downscaling of data is required. A Linear Model (Smith and Barstad, 2004) is used to dynamically downscale orographic precipitation over Western Norway from twelve General Circulation Model simulations based on the A1B emissions scenario (IPCC, 2003). An assessment of the changes to future Orographic Precipitation (2046–2065 and 2081–2100 time periods) versus the historical control period (1971–2000) is carried out. Results show an increased number of Orographic Precipitation days and an increased Orographic Precipitation intensity. Extreme precipitation events are up to 20% more intense than the 1971–2000 values. Extremes are defined by the exceedence of the 99%-ile threshold in the time slice. Using station-based observations from the control period, the results from downscaling can be used to generate simulated precipitation histograms at selected stations. The Linear Model approach also allows for simulated changes in precipitation to be disaggregated according to their causal source: (a) the role of topography and (b) changes to the amount of moisture delivery to the site. The latter can be additionaly separated into moisture content changes due to: (i) temperature; (ii) wind speed; (iii) stability. An analysis of these results suggests a strong role for warming in increasing the intensity of extreme Orographic Precipitation events in the area.

scholarly journals Semiarid grasslands and extreme precipitation events: Do experimental results scale to the landscape?

Ecology ◽  
2021 ◽  
Author(s):  
Alison K. Post ◽  
Kristin P. Davis ◽  
Jillian LaRoe ◽  
David L. Hoover ◽  
Alan K. Knapp
Keyword(s):  
Extreme Precipitation ◽  
Experimental Results ◽  
Extreme Precipitation Events ◽  
Precipitation Events
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