On the role of CO2 in enhancing the temporal clustering of heavy precipitation across Europe

Author(s):  
Zhiqi Yang ◽  
Gabriele Villarini

<p>Heavy precipitation has increased across many areas of the world, not only in terms of amounts but also of intensity and frequency, causing billions of dollars in economic losses and numerous fatalities. Our ability to prepare for and adapt to these events is tied to our understanding of the physical processes responsible for these events, and how they may respond to changes in anthropogenic forcings. Here we focus on the temporal clustering of heavy precipitation across Europe, highlight what the major climate drivers responsible for it are, and how it may change in response to changes in the concentration of greenhouse gasses. More specifically, we use a peak over threshold approach to identify heavy precipitation events, and Cox regression to relate the occurrence of these events to four climate modes that have been connected with the occurrence of heavy precipitation across Europe: the Arctic Oscillation (AO), the North Atlantic Oscillation (NAO), the East Atlantic (EA) pattern, and the Scandinavia pattern (SCAND). We use outputs from the Coupled Model Intercomparison Project Phase 5 (CMIP5), and experiments that allow us to focus on the response to CO<sub>2</sub> (pre-industrial, 1pctCO<sub>2</sub>, abrupt4×CO<sub>2</sub>). To further detect the effects of downscaling on model-simulated precipitation, we also considered the accuracy of the EURO-CORDEX regional climate model (RCM) on capturing the temporal clustering in heavy precipitation across Europe. We find that: 1) the CMIP5 models can capture the temporal clustering in heavy precipitation across Europe as a function of these four climate modes; 2) the increases in CO<sub>2</sub> are expected to lead to a strengthening of the relationship between the climate modes and the occurrence of heavy precipitation events; 3) the response to an abrupt increase in CO<sub>2</sub> is generally stronger compared to a more gradual one.</p>

2014 ◽  
Vol 27 (15) ◽  
pp. 5941-5963 ◽  
Author(s):  
Xiang Gao ◽  
C. Adam Schlosser ◽  
Pingping Xie ◽  
Erwan Monier ◽  
Dara Entekhabi

Abstract An analogue method is presented to detect the occurrence of heavy precipitation events without relying on modeled precipitation. The approach is based on using composites to identify distinct large-scale atmospheric conditions associated with widespread heavy precipitation events across local scales. These composites, exemplified in the south-central, midwestern, and western United States, are derived through the analysis of 27-yr (1979–2005) Climate Prediction Center (CPC) gridded station data and the NASA Modern-Era Retrospective Analysis for Research and Applications (MERRA). Circulation features and moisture plumes associated with heavy precipitation events are examined. The analogues are evaluated against the relevant daily meteorological fields from the MERRA reanalysis and achieve a success rate of around 80% in detecting observed heavy events within one or two days. The method also captures the observed interannual variations of seasonal heavy events with higher correlation and smaller RMSE than MERRA precipitation. When applied to the same 27-yr twentieth-century climate model simulations from Phase 5 of the Coupled Model Intercomparison Project (CMIP5), the analogue method produces a more consistent and less uncertain number of seasonal heavy precipitation events with observation as opposed to using model-simulated precipitation. The analogue method also performs better than model-based precipitation in characterizing the statistics (minimum, lower and upper quartile, median, and maximum) of year-to-year seasonal heavy precipitation days. These results indicate the capability of CMIP5 models to realistically simulate large-scale atmospheric conditions associated with widespread local-scale heavy precipitation events with a credible frequency. Overall, the presented analyses highlight the improved diagnoses of the analogue method against an evaluation that considers modeled precipitation alone to assess heavy precipitation frequency.


2015 ◽  
Vol 28 (15) ◽  
pp. 6193-6203 ◽  
Author(s):  
Enrico Scoccimarro ◽  
Gabriele Villarini ◽  
Marcello Vichi ◽  
Matteo Zampieri ◽  
Pier Giuseppe Fogli ◽  
...  

Abstract Heavy precipitation is a major hazard over Europe. It is well established that climate model projections indicate a tendency toward more extreme daily rainfall events. It is still uncertain, however, how this changing intensity translates at the subdaily time scales. The main goal of the present study is to examine possible differences in projected changes in intense precipitation events over Europe at the daily and subdaily (3-hourly) time scales using a state-of-the-science climate model. The focus will be on one representative concentration pathway (RCP8.5), considered as illustrative of a high rate of increase in greenhouse gas concentrations over this century. There are statistically significant differences in intense precipitation projections (up to 40%) when comparing the results at the daily and subdaily time scales. Over northeastern Europe, projected precipitation intensification at the 3-hourly scale is lower than at the daily scale. On the other hand, Spain and the western seaboard exhibit an opposite behavior, with stronger intensification at the 3-hourly scale rather than the daily scale. While the mean properties of the precipitation distributions are independent of the analyzed frequency, projected precipitation intensification exhibits regional differences. This finding has implications for the extrapolation of impacts of intense precipitation events, given the daily time scale at which the analyses are usually performed.


2014 ◽  
Vol 16 (3) ◽  
pp. 595-602 ◽  
Author(s):  
Enrico Scoccimarro ◽  
Silvio Gualdi ◽  
Alessio Bellucci ◽  
Matteo Zampieri ◽  
Antonio Navarra

2013 ◽  
Vol 26 (10) ◽  
pp. 3209-3230 ◽  
Author(s):  
Anthony M. DeAngelis ◽  
Anthony J. Broccoli ◽  
Steven G. Decker

Abstract Climate model simulations of daily precipitation statistics from the third phase of the Coupled Model Intercomparison Project (CMIP3) were evaluated against precipitation observations from North America over the period 1979–99. The evaluation revealed that the models underestimate the intensity of heavy and extreme precipitation along the Pacific coast, southeastern United States, and southern Mexico, and these biases are robust among the models. The models also overestimate the intensity of light precipitation events over much of North America, resulting in fairly realistic mean precipitation in many places. In contrast, heavy precipitation is simulated realistically over northern and eastern Canada, as is the seasonal cycle of heavy precipitation over a majority of North America. An evaluation of the simulated atmospheric dynamics and thermodynamics associated with extreme precipitation events was also conducted using the North American Regional Reanalysis (NARR). The models were found to capture the large-scale physical mechanisms that generate extreme precipitation realistically, although they tend to overestimate the strength of the associated atmospheric circulation features. This suggests that climate model deficiencies such as insufficient spatial resolution, inadequate representation of convective precipitation, and overly smoothed topography may be more important for biases in simulated heavy precipitation than errors in the large-scale circulation during extreme events.


2021 ◽  
Author(s):  
Moshe Armon ◽  
Francesco Marra ◽  
Chaim Garfinkel ◽  
Dorita Rostkier-Edelstein ◽  
Ori Adam ◽  
...  

<p>Heavy precipitation events (HPEs) in the densely populated eastern Mediterranean trigger natural hazards, such as flash floods and urban flooding. However, they also supply critical amounts of fresh water to this desert-bounded region. The impact of global warming on such events is thus vital to the inhabitants of the region. HPEs are poorly represented in global climate models, leading to large uncertainty in their sensitivity to climate change. Is total rainfall in HPEs decreasing, as projected for the mean annual rainfall? Are short duration rain rates decreasing, or rather increasing as expected from the higher atmospheric moisture content? Where are the changes more pronounced, near the sea or farther inland towards the desert? To answer these questions, we have identified 41 historical HPEs from a long weather radar record (1990-2014) and simulated them in the same resolution (1 km<sup>2</sup>) using the convection-permitting weather research and forecasting (WRF) model. Results were validated versus the radar data, and served as a control group to simulations of the same events under ‘pseudo global warming’ (PGW) conditions. The PGW methodology we use imposes results from the ensemble mean of 29 Coupled Model Intercomparison Project Phase 5 (CMIP5) models for the end of the century on the initial and boundary conditions of each event simulated. The results indicate that HPEs in the future may become more temporally focused: they are 6% shorter and exhibit maximum local short-duration rain rates which are ~20% higher on average, with larger values over the sea and the wetter part of the region, and smaller over the desert. However, they are also much drier; total precipitation during the future-simulated HPEs decreases substantially (~-20%) throughout the eastern Mediterranean. The meteorological factors leading to this decrease include shallower cyclones and the projected differential land-sea warming, which causes reduced relative humidity over land. These changing rainfall patterns are expected to amplify water scarcity – a known nexus of conflict and strife in the region – highlighting the urgent need for deeper knowledge, and the implementation of adaptation and mitigation strategies.</p>


2020 ◽  
Vol 33 (16) ◽  
pp. 7155-7178
Author(s):  
Jiao Chen ◽  
Aiguo Dai ◽  
Yaocun Zhang

AbstractLight–moderate precipitation is projected to decrease whereas heavy precipitation may increase under greenhouse gas (GHG)-induced global warming, while atmospheric convective available potential energy (CAPE) over most of the globe and convective inhibition (CIN) over land are projected to increase. The underlying processes for these precipitation changes are not fully understood. Here, projected precipitation changes are analyzed using 3-hourly data from simulations by a fully coupled climate model, and their link to the CAPE and CIN changes is examined. The model approximately captures the spatial patterns in the mean precipitation frequencies and the significant correlation between the precipitation frequencies or intensity and CAPE over most of the globe or CIN over tropical oceans seen in reanalysis, and it projects decreased light–moderate precipitation (0.01 < P ≤ 1 mm h−1) but increased heavy precipitation (P > 1 mm h−1) in a warmer climate. Results show that most of the light–moderate precipitation events occur under low-CAPE and/or low-CIN conditions, which are projected to decrease greatly in a warmer climate as increased temperature and humidity shift many of such cases into moderate–high CAPE or CIN cases. This results in large decreases in the light–moderate precipitation events. In contrast, increases in heavy precipitation result primarily from its increased probability under given CAPE and CIN, with a secondary contribution from the CAPE/CIN frequency changes. The increased probability for heavy precipitation partly results from a shift of the precipitation histogram toward higher intensity that could result from a uniform percentage increase in precipitation intensity due to increased water vapor in a warmer climate.


2013 ◽  
Vol 14 (5) ◽  
pp. 1500-1514 ◽  
Author(s):  
Dimitrios Stampoulis ◽  
Emmanouil N. Anagnostou ◽  
Efthymios I. Nikolopoulos

Abstract Heavy precipitation events (HPE) can incur significant economic losses as well as losses of lives through catastrophic floods. Evidence of increasing heavy precipitation at continental and global scales clearly emphasizes the need to accurately quantify these phenomena. The current study focuses on the error analysis of two of the main quasi-global, high-resolution satellite products [Climate Prediction Center (CPC) morphing technique (CMORPH) and Precipitation Estimation from Remotely Sensed Imagery Using Artificial Neural Networks (PERSIANN)], using rainfall data derived from high-quality weather radar rainfall estimates as a reference. This analysis is based on seven major flood-inducing HPEs that developed over complex terrain areas in northern Italy (Fella and Sessia regions) and southern France (Cevennes–Vivarais region). The storm cases were categorized as convective or stratiform based on their characteristics, including rainfall intensity, duration, and area coverage. The results indicate that precipitation type has an effect on the algorithm's ability to capture rainfall effectively. Convective storm cases exhibited greater rain rate retrieval errors, while low rain rates in stratiform-type systems are not well captured by the satellite algorithms investigated in this study, thus leading to greater missed rainfall volumes. Overall, CMORPH exhibited better error statistics than PERSIANN for the HPEs of this study. Similarities are also shown in the two satellite products' error characteristics for the HPEs that occurred in the same geographical area.


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