scholarly journals Effects of Global Warming on Heavy Rainfall During the Baiu Season Projected by a Cloud-System-Resolving Model

2008 ◽  
Vol 3 (1) ◽  
pp. 15-24 ◽  
Author(s):  
Masaomi Nakamura ◽  
◽  
Sachie Kaneda ◽  
Yasutaka Wakazuki ◽  
Chiashi Muroi ◽  
...  

Under the Kyosei-4 Project, unprecedented high resolution global and regional climate models were developed on the Earth Simulator to investigate the effect of global warming on tropical cyclones, baiu frontal rainfall systems, and heavy rainfall events that could not be resolved using conventional climate models.For the regional climate model, a nonhydrostatic model (NHM) with a horizontal resolution of 5 km was developed to be used in the simulation of heavy rainfall during the baiu season in Japan. Simulations in June and July were executed for 10 years in present and future global warming climates. It was found that, due to global warming, mean rainfall is projected to increase except in eastern and northern Japan, the frequency of heavy rainfall events would increase and its increment rate become higher for heavier rainfall, and return values for extreme rainfall would grow.Experiments using an NHM with a horizontal resolution of 1 km were conducted to study the effects of resolution. Compared to 5 km resolution, it expresses the organization of rainfall systems causing heavy rainfall and the appearance-frequency distribution of rainfall for variable intensities more realistically.

Water ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 833
Author(s):  
Ernest Amoussou ◽  
Hervé Awoye ◽  
Henri S. Totin Vodounon ◽  
Salomon Obahoundje ◽  
Pierre Camberlin ◽  
...  

This study characterizes the future changes in extreme rainfall and air temperature in the Mono river basin where the main economic activity is weather dependent and local populations are highly vulnerable to natural hazards, including flood inundations. Daily precipitation and temperature from observational datasets and Regional Climate Models (RCMs) output from REMO, RegCM, HadRM3, and RCA were used to analyze climatic variations in space and time, and fit a GEV model to investigate the extreme rainfalls and their return periods. The results indicate that the realism of the simulated climate in this domain is mainly controlled by the choice of the RCMs. These RCMs projected a 1 to 1.5 °C temperature increase by 2050 while the projected trends for cumulated precipitation are null or very moderate and diverge among models. Contrasting results were obtained for the intense rainfall events, with RegCM and HadRM3 pointing to a significant increase in the intensity of extreme rainfall events. The GEV model is well suited for the prediction of heavy rainfall events although there are uncertainties beyond the 90th percentile. The annual maxima of daily precipitation will also increase by 2050 and could be of benefit to the ecosystem services and socioeconomic activities in the Mono river basin but could also be a threat.


Atmosphere ◽  
2018 ◽  
Vol 9 (7) ◽  
pp. 262 ◽  
Author(s):  
Coraline Wyard ◽  
Sébastien Doutreloup ◽  
Alexandre Belleflamme ◽  
Martin Wild ◽  
Xavier Fettweis

The use of regional climate models (RCMs) can partly reduce the biases in global radiative flux (Eg↓) that are found in reanalysis products and global models, as they allow for a finer spatial resolution and a finer parametrisation of surface and atmospheric processes. In this study, we assess the ability of the MAR («Modèle Atmosphérique Régional») RCM to reproduce observed changes in Eg↓, and we investigate the added value of MAR with respect to reanalyses. Simulations were performed at a horizontal resolution of 5 km for the period 1959–2010 by forcing MAR with different reanalysis products: ERA40/ERA-interim, NCEP/NCAR-v1, ERA-20C, and 20CRV2C. Measurements of Eg↓ from the Global Energy Balance Archive (GEBA) and from the Royal Meteorological Institute of Belgium (RMIB), as well as cloud cover observations from Belgocontrol and RMIB, were used for the evaluation of the MAR model and the forcing reanalyses. Results show that MAR enables largely reducing the mean biases that are present in the reanalyses. The trend analysis shows that only MAR forced by ERA40/ERA-interim shows historical trends, which is probably because the ERA40/ERA-interim has a better horizontal resolution and assimilates more observations than the other reanalyses that are used in this study. The results suggest that the solar brightening observed since the 1980s in Belgium has mainly been due to decreasing cloud cover.


2011 ◽  
Vol 11 (9) ◽  
pp. 2463-2468 ◽  
Author(s):  
Y. Tramblay ◽  
L. Neppel ◽  
J. Carreau

Abstract. In Mediterranean regions, climate studies indicate for the future a possible increase in the extreme rainfall events occurrence and intensity. To evaluate the future changes in the extreme event distribution, there is a need to provide non-stationary models taking into account the non-stationarity of climate. In this study, several climatic covariates are tested in a non-stationary peaks-over-threshold modeling approach for heavy rainfall events in Southern France. Results indicate that the introduction of climatic covariates could improve the statistical modeling of extreme events. In the case study, the frequency of southern synoptic circulation patterns is found to improve the occurrence process of extreme events modeled via a Poisson distribution, whereas for the magnitude of the events, the air temperature and sea level pressure appear as valid covariates for the Generalized Pareto distribution scale parameter. Covariates describing the humidity fluxes at monthly and seasonal time scales also provide significant model improvements for the occurrence and the magnitude of heavy rainfall events. With such models including climatic covariates, it becomes possible to asses the risk of extreme events given certain climatic conditions at monthly or seasonal timescales. The future changes in the heavy rainfall distribution can also be evaluated using covariates computed by climate models.


2021 ◽  
pp. 1-56

This paper describes the downscaling of an ensemble of twelve GCMs using the WRF model at 12-km grid spacing over the period 1970-2099, examining the mesoscale impacts of global warming as well as the uncertainties in its mesoscale expression. The RCP 8.5 emissions scenario was used to drive both global and regional climate models. The regional climate modeling system reduced bias and improved realism for a historical period, in contrast to substantial errors for the GCM simulations driven by lack of resolution. The regional climate ensemble indicated several mesoscale responses to global warming that were not apparent in the global model simulations, such as enhanced continental interior warming during both winter and summer as well as increasing winter precipitation trends over the windward slopes of regional terrain, with declining trends to the lee of major barriers. During summer there is general drying, except to the east of the Cascades. April 1 snowpack declines are large over the lower to middle slopes of regional terrain, with small snowpack increases over the lower elevations of the interior. Snow-albedo feedbacks are very different between GCM and RCM projections, with the GCM’s producing large, unphysical areas of snowpack loss and enhanced warming. Daily average winds change little under global warming, but maximum easterly winds decline modestly, driven by a preferential sea level pressure decline over the continental interior. Although temperatures warm continuously over the domain after approximately 2010, with slight acceleration over time, occurrences of temperature extremes increase rapidly during the second half of the 21st century.


2021 ◽  
Author(s):  
Dang Nguyen Dong Phuong ◽  
Nguyen Thi Huyen ◽  
Nguyen Duy Liem ◽  
Nguyen Thi Hong ◽  
Dang Kien Cuong ◽  
...  

Abstract Understanding past changes in the characteristics of climate extremes (such as frequency, intensity, and duration) forms an essential part of viable countermeasures to cope with climate-induced risks under a rapidly warming world. Thus, this paper endeavored to explore possible non-monotonic trend components in heavy rainfall events over the Central Highlands of Vietnam by employing the Şen’s innovative trend analysis (ITA) method in conjunction with the well-defined extreme rainfall indices developed by the Joint CCl/CLIVAR/JCOMM Expert Team on Climate Change Detection and Indices (ETCCDI). The outcomes show that the overall trends in most extreme rainfall indices exhibited significant increases at several stations. Moreover, the high-value subgroups of most analyzed indices (such as maximum 5-day precipitation amount (Rx5day), simple daily intensity index (SDII), very wet days (R95p), extremely wet days (R99p), number of extremely heavy precipitation days (R50mm), and consecutive dry days (CDD)) were characterized mainly by significant increasing trends, thereby implying that heavy rainfall events have become more frequent and intense over recent decades. Some stations also exposed significant increasing trend behaviors in a given extreme index within all low-, medium-, and high-value subgroups. In general, it is expected that these findings yield more insightful knowledge on rainfall extremes to local decision-makers and other stakeholders.


2018 ◽  
Author(s):  
Ruksana H. Rimi ◽  
Karsten Haustein ◽  
Emily J. Barbour ◽  
Sarah N. Sparrow ◽  
Sihan Li ◽  
...  

Abstract. Anthropogenic climate change is likely to increase the frequency of extreme weather events in future. Previous studies have robustly shown how and where climate change has already changed the risks of weather extremes. However, developing countries have been somewhat underrepresented in these studies, despite high vulnerability and limited capacities to adapt. How additional global warming would affect the future risks of extreme rainfall events in Bangladesh needs to be addressed to limit adverse impacts. Our study focuses on understanding and quantifying the relative risks of seasonal extreme rainfall events in Bangladesh under the Paris Agreement temperature goals of 1.5 °C and 2 °C warming above pre-industrial levels. In particular, we investigate the influence of anthropogenic aerosols on these risks given their likely future reduction and resulting amplification of global warming. Using large ensemble regional climate model simulations from weather@home under different forcing scenarios, we compare the risks of rainfall events under pre-industrial (natural), current (actual), 1.5 °C, and 2.0 °C warmer and greenhouse gas only (anthropogenic aerosols removed) conditions. We find that the risk of a 1 in 100 year rainfall event has already increased significantly compared with pre-industrial levels across parts of Bangladesh, with additional increases likely for 1.5 and 2.0 degree warming (of up to 5.5 times higher, with an uncertainty range of 3.5 to 7.8 times). Impacts were observed during both the pre-monsoon and monsoon periods, but were spatially variable across the country in terms of the level of impact. Results also show that reduction in anthropogenic aerosols plays an important role in determining the overall future climate change impacts; by exacerbating the effects of GHG induced global warming and thereby increasing the rainfall intensity. We highlight that the net aerosol effect varies from region to region within Bangladesh, which leads to different outcomes of aerosol reduction on extreme rainfall statistics, and must therefore be considered in future risk assessments. Whilst there is a substantial reduction in the impacts resulting from 1.5 °C compared with 2 °C warming, the difference is spatially and temporally variable, specifically with respect to seasonal extreme rainfall events.


2019 ◽  
Vol 23 (7) ◽  
pp. 2863-2875 ◽  
Author(s):  
Sungmin O ◽  
Ulrich Foelsche

Abstract. Hydrology and remote-sensing communities have made use of dense rain-gauge networks for studying rainfall uncertainty and variability. However, in most regions, these dense networks are only available at small spatial scales (e.g., within remote-sensing subpixel areas) and over short periods of time. Just a few studies have applied a similar approach, i.e., employing dense gauge networks to catchment-scale areas, which limits the verification of their results in other regions. Using 10-year rainfall measurements from a network of 150 rain gauges, WegenerNet (WEGN), we assess the spatial uncertainty in observed heavy rainfall events. The WEGN network is located in southeastern Austria over an area of 20 km × 15 km with moderate orography. First, the spatial variability in rainfall in the region was characterized using a correlogram at daily and sub-daily scales. Differences in the spatial structure of rainfall events between warm and cold seasons are apparent, and we selected heavy rainfall events, the upper 10 % of wettest days during the warm season, for further analyses because of their high potential for causing hazards. Secondly, we investigated the uncertainty in estimating mean areal rainfall arising from a limited gauge density. The average number of gauges required to obtain areal rainfall with errors less than a certain threshold (≤20 % normalized root-mean-square error – RMSE – is considered here) tends to increase, roughly following a power law as the timescale decreases, while the errors can be significantly reduced by establishing regularly distributed gauges. Lastly, the impact of spatial aggregation on extreme rainfall was examined, using gridded rainfall data with various horizontal grid spacings. The spatial-scale dependence was clearly observed at high intensity thresholds and high temporal resolutions; e.g., the 5 min extreme intensity increases by 44 % for the 99.9th and by 25 % for the 99th percentile, with increasing horizontal resolution from 0.1 to 0.01∘. Quantitative uncertainty information from this study can guide both data users and producers to estimate uncertainty in their own observational datasets, consequently leading to the sensible use of the data in relevant applications. Our findings could be transferred to midlatitude regions with moderate topography, but only to a limited extent, given that regional factors that can affect rainfall type and process are not explicitly considered in the study.


2020 ◽  
Author(s):  
Akash Koppa ◽  
Thomas Remke ◽  
Stephan Thober ◽  
Oldrich Rakovec ◽  
Sebastian Müller ◽  
...  

<p>Headwater systems are a major source of water, sediments, and nutrients (including nitrogen and carbon di-oxide) for downstream aquatic, riparian, and inland ecosystems. As precipitation changes are expected to exhibit considerable spatial variability in the future, we hypothesize that headwater contribution to major rivers will also change significantly. Quantifying these changes is essential for developing effective adaptation and mitigation strategies against climate change. However, the lack of hydrologic projections at high resolutions over large domains have hindered attempts to quantify climate change impacts on headwater systems.</p><p>Here, we overcome this challenge by developing an ensemble of hydrologic projections at an unprecedented resolution (1km) for Germany. These high resolution projections are developed within the framework of the Helmholtz Climate Initiative (https://www.helmholtz.de/en/current-topics/the-initiative/climate-research/). Our modeling chain consists of the following four components:</p><p><strong>Climate Modeling:</strong> We statistically downscale and bias-adjust climate change scenarios from three representative concentration pathway (RCP) scenarios derived from the EURO-CORDEX ensemble - 2.6, 4.5, and 8.5 to a horizontal resolution of 1km over Germany (i.e, a total of 75 ensemble members). The EURO-CORDEX ensemble is generated by dynamically downscaling CMIP-5 general circulation models (GCM) using regional climate models (RCMs). <strong>Hydrologic Modeling:</strong> To account for model structure uncertainty, the climate model projections are used as forcings for three spatially distributed hydrologic models - a) the mesocale Hydrologic model (mHM), b) Noah-MP, and c) HTESSEL. The outputs that will be generated in the study are soil moisture, evapotranspiration, snow water equivalent, and runoff. <strong>Streamflow Routing:</strong> To minimize uncertainty from river routing schemes, we use the multiscale routing model (mRM v1.0) to route runoff from all the three models. <strong>River Temperature Modeling:</strong> A novel river temperature model is used to quantify the changes in river temperature due to anthropogenic warming.</p><p>The 225-member ensemble streamflow outputs (75 climate model members and 3 hydrologic models) are used to quantify the changes in the contribution of headwater watersheds to all the major rivers in Germany. Finally, we analyze changes in soil moisture, snow melt, and river temperature and their implications for headwater contributions. Previously, a high-resolution (5km) multi-model ensemble for climate change projections has been created within the EDgE project<strong><sup>1,2,3,4</sup></strong>. The newly created projections in this project will be compared against those created in the EDgE project.  The ensemble used in this project will profit from the higher resolution of the regional climate models that provide a more detailed land orography.</p><p><strong>References</strong></p><p><strong>[1] </strong>Marx,<em> A. et al. (2018). Climate change alters low flows in Europe under global warming of 1.5, 2, and 3 C. Hydrology and Earth System Sciences, 22(2), 1017-1032.</em></p><p><strong>[2]</strong><em> Samaniego, L. et al. (2019). Hydrological forecasts and projections for improved decision-making in the water sector in Europe. Bulletin of the American Meteorological Society.</em></p><p><strong>[3]</strong> Samaniego,<em> L. and Thober, S., et al. (2018). Anthropogenic warming exacerbates European soil moisture droughts. Nature Climate Change, 8(5), 421.</em></p><p><strong>[4]</strong> Thober,<em> S. et al. (2018). Multi-model ensemble projections of European river floods and high flows at 1.5, 2, and 3 degrees global warming. Environmental Research Letters, 13(1), 014003.</em></p><p> </p><p> </p><p> </p>


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