The Mesoscale Response to Global Warming over the Pacific Northwest Evaluated Using a Regional Climate Model Ensemble

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):  
Paola Nanni ◽  
David J. Peres ◽  
Rosaria E. Musumeci ◽  
Antonino Cancelliere

<p>Climate change is a phenomenon that is claimed to be responsible for a significant alteration of the precipitation regime in different regions worldwide and for the induced potential changes on related hydrological hazards. In particular, some consensus has raised about the fact that climate changes can induce a shift to shorter but more intense rainfall events, causing an intensification of urban and flash flooding hazards.  Regional climate models (RCMs) are a useful tool for trying to predict the impacts of climate change on hydrological events, although their application may lead to significant differences when different models are adopted. For this reason, it is of key importance to ascertain the quality of regional climate models (RCMs), especially with reference to their ability to reproduce the main climatological regimes with respect to an historical period. To this end, several studies have focused on the analysis of annual or monthly data, while few studies do exist that analyze the sub-daily data that are made available by the regional climate projection initiatives. In this study, with reference to specific locations in eastern Sicily (Italy), we first evaluate historical simulations of precipitation data from selected RCMs belonging to the Euro-CORDEX (Coordinated Regional Climate Downscaling Experiment for the Euro-Mediterranean area) with high temporal resolution (three-hourly), in order to understand how they compare to fine-resolution observations. In particular, we investigate the ability to reproduce rainfall event characteristics, as well as annual maxima precipitation at different durations. With reference to rainfall event characteristics, we specifically focus on duration, intensity, and inter-arrival time between events. Annual maxima are analyzed at sub-daily durations. We then analyze the future simulations according to different Representative concentration scenarios. The proposed analysis highlights the differences between the different RCMs, supporting the selection of the most suitable climate model for assessing the impacts in the considered locations, and to understand what trends for intense precipitation are to be expected in the future.</p>


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>


2017 ◽  
Vol 30 (17) ◽  
pp. 6771-6794 ◽  
Author(s):  
Tomáš Púčik ◽  
Pieter Groenemeijer ◽  
Anja T. Rädler ◽  
Lars Tijssen ◽  
Grigory Nikulin ◽  
...  

The occurrence of environmental conditions favorable for severe convective storms was assessed in an ensemble of 14 regional climate models covering Europe and the Mediterranean with a horizontal grid spacing of 0.44°. These conditions included the collocated presence of latent instability and strong deep-layer (surface to 500 hPa) wind shear, which is conducive to the severe and well-organized convective storms. The occurrence of precipitation in the models was used as a proxy for convective initiation. Two climate scenarios (RCP4.5 and RCP8.5) were investigated by comparing two future periods (2021–50 and 2071–2100) to a historical period (1971–2000) for each of these scenarios. The ensemble simulates a robust increase (change larger than twice the ensemble sample standard deviation) in the frequency of occurrence of unstable environments (lifted index ≤ −2) across central and south-central Europe in the RCP8.5 scenario in the late twenty-first century. This increase coincides with the increase in lower-tropospheric moisture. Smaller, less robust changes were found until midcentury in the RCP8.5 scenario and in the RCP4.5 scenario. Changes in the frequency of situations with strong (≥15 m s−1) deep-layer shear were found to be small and not robust, except across far northern Europe, where a decrease in shear is projected. By the end of the century, the simultaneous occurrence of latent instability, strong deep-layer shear, and model precipitation is simulated to increase by up to 100% across central and eastern Europe in the RCP8.5 and by 30%–50% in the RCP4.5 scenario. Until midcentury, increases in the 10%–25% range are forecast for most regions. A large intermodel variability is present in the ensemble and is primarily due to the uncertainties in the frequency of the occurrence of unstable environments.


2015 ◽  
Vol 19 (2) ◽  
pp. 711-728 ◽  
Author(s):  
J. Teng ◽  
N. J. Potter ◽  
F. H. S. Chiew ◽  
L. Zhang ◽  
B. Wang ◽  
...  

Abstract. Many studies bias correct daily precipitation from climate models to match the observed precipitation statistics, and the bias corrected data are then used for various modelling applications. This paper presents a review of recent methods used to bias correct precipitation from regional climate models (RCMs). The paper then assesses four bias correction methods applied to the weather research and forecasting (WRF) model simulated precipitation, and the follow-on impact on modelled runoff for eight catchments in southeast Australia. Overall, the best results are produced by either quantile mapping or a newly proposed two-state gamma distribution mapping method. However, the differences between the methods are small in the modelling experiments here (and as reported in the literature), mainly due to the substantial corrections required and inconsistent errors over time (non-stationarity). The errors in bias corrected precipitation are typically amplified in modelled runoff. The tested methods cannot overcome limitations of the RCM in simulating precipitation sequence, which affects runoff generation. Results further show that whereas bias correction does not seem to alter change signals in precipitation means, it can introduce additional uncertainty to change signals in high precipitation amounts and, consequently, in runoff. Future climate change impact studies need to take this into account when deciding whether to use raw or bias corrected RCM results. Nevertheless, RCMs will continue to improve and will become increasingly useful for hydrological applications as the bias in RCM simulations reduces.


Author(s):  
Weijia Qian ◽  
Howard H. Chang

Health impact assessments of future environmental exposures are routinely conducted to quantify population burdens associated with the changing climate. It is well-recognized that simulations from climate models need to be bias-corrected against observations to estimate future exposures. Quantile mapping (QM) is a technique that has gained popularity in climate science because of its focus on bias-correcting the entire exposure distribution. Even though improved bias-correction at the extreme tails of exposure may be particularly important for estimating health burdens, the application of QM in health impact projection has been limited. In this paper we describe and apply five QM methods to estimate excess emergency department (ED) visits due to projected changes in warm-season minimum temperature in Atlanta, USA. We utilized temperature projections from an ensemble of regional climate models in the North American-Coordinated Regional Climate Downscaling Experiment (NA-CORDEX). Across QM methods, we estimated consistent increase in ED visits across climate model ensemble under RCP 8.5 during the period 2050 to 2099. We found that QM methods can significantly reduce between-model variation in health impact projections (50–70% decreases in between-model standard deviation). Particularly, the quantile delta mapping approach had the largest reduction and is recommended also because of its ability to preserve model-projected absolute temporal changes in quantiles.


2018 ◽  
Vol 22 (1) ◽  
pp. 673-687 ◽  
Author(s):  
Antoine Colmet-Daage ◽  
Emilia Sanchez-Gomez ◽  
Sophie Ricci ◽  
Cécile Llovel ◽  
Valérie Borrell Estupina ◽  
...  

Abstract. The climate change impact on mean and extreme precipitation events in the northern Mediterranean region is assessed using high-resolution EuroCORDEX and MedCORDEX simulations. The focus is made on three regions, Lez and Aude located in France, and Muga located in northeastern Spain, and eight pairs of global and regional climate models are analyzed with respect to the SAFRAN product. First the model skills are evaluated in terms of bias for the precipitation annual cycle over historical period. Then future changes in extreme precipitation, under two emission scenarios, are estimated through the computation of past/future change coefficients of quantile-ranked model precipitation outputs. Over the 1981–2010 period, the cumulative precipitation is overestimated for most models over the mountainous regions and underestimated over the coastal regions in autumn and higher-order quantile. The ensemble mean and the spread for future period remain unchanged under RCP4.5 scenario and decrease under RCP8.5 scenario. Extreme precipitation events are intensified over the three catchments with a smaller ensemble spread under RCP8.5 revealing more evident changes, especially in the later part of the 21st century.


2021 ◽  
Author(s):  
Jeremy Carter ◽  
Amber Leeson ◽  
Andrew Orr ◽  
Christoph Kittel ◽  
Melchior van Wessem

<p>Understanding the surface climatology of the Antarctic ice sheet is essential if we are to adequately predict its response to future climate change. This includes both primary impacts such as increased ice melting and secondary impacts such as ice shelf collapse events. Given its size, and inhospitable environment, weather stations on Antarctica are sparse. Thus, we rely on regional climate models to 1) develop our understanding of how the climate of Antarctica varies in both time and space and 2) provide data to use as context for remote sensing studies and forcing for dynamical process models. Given that there are a number of different regional climate models available that explicitly simulate Antarctic climate, understanding inter- and intra model variability is important.</p><p>Here, inter- and intra-model variability in Antarctic-wide regional climate model output is assessed for: snowfall; rainfall; snowmelt and near-surface air temperature within a cloud-based virtual lab framework. State-of-the-art regional climate model runs from the Antarctic-CORDEX project using the RACMO, MAR and MetUM models are used, together with the ERA5 and ERA-Interim reanalyses products. Multiple simulations using the same model and domain boundary but run at either different spatial resolutions or with different driving data are used. Traditional analysis techniques are exploited and the question of potential added value from more modern and involved methods such as the use of Gaussian Processes is investigated. The advantages of using a virtual lab in a cloud based environment for increasing transparency and reproducibility, are demonstrated, with a view to ultimately make the code and methods used widely available for other research groups.</p>


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.


2021 ◽  
Author(s):  
Antoine Doury ◽  
Samuel Somot ◽  
Sébastien Gadat ◽  
Aurélien Ribes ◽  
Lola Corre

Abstract Providing reliable information on climate change at local scale remains a challenge of first importance for impact studies and policymakers. Here, we propose a novel hybrid downscaling method combining the strengths of both empirical statistical downscaling methods and Regional Climate Models (RCMs). The aim of this tool is to enlarge the size of high-resolution RCM simulation ensembles at low cost.We build a statistical RCM-emulator by estimating the downscaling function included in the RCM. This framework allows us to learn the relationship between large-scale predictors and a local surface variable of interest over the RCM domain in present and future climate. Furthermore, the emulator relies on a neural network architecture, which grants computational efficiency. The RCM-emulator developed in this study is trained to produce daily maps of the near-surface temperature at the RCM resolution (12km). The emulator demonstrates an excellent ability to reproduce the complex spatial structure and daily variability simulated by the RCM and in particular the way the RCM refines locally the low-resolution climate patterns. Training in future climate appears to be a key feature of our emulator. Moreover, there is a huge computational benefit in running the emulator rather than the RCM, since training the emulator takes about 2 hours on GPU, and the prediction is nearly instantaneous. However, further work is needed to improve the way the RCM-emulator reproduces some of the temperature extremes, the intensity of climate change, and to extend the proposed methodology to different regions, GCMs, RCMs, and variables of interest.


2021 ◽  
Author(s):  
Gaby S. Langendijk ◽  
Diana Rechid ◽  
Daniela Jacob

<p>Urban areas are prone to climate change impacts. A transition towards sustainable and climate-resilient urban areas is relying heavily on useful, evidence-based climate information on urban scales. However, current climate data and information produced by urban or climate models are either not scale compliant for cities, or do not cover essential parameters and/or urban-rural interactions under climate change conditions. Furthermore, although e.g. the urban heat island may be better understood, other phenomena, such as moisture change, are little researched. Our research shows the potential of regional climate models, within the EURO-CORDEX framework, to provide climate projections and information on urban scales for 11km and 3km grid size. The city of Berlin is taken as a case-study. The results on the 11km spatial scale show that the regional climate models simulate a distinct difference between Berlin and its surroundings for temperature and humidity related variables. There is an increase in urban dry island conditions in Berlin towards the end of the 21st century. To gain a more detailed understanding of climate change impacts, extreme weather conditions were investigated under a 2°C global warming and further downscaled to the 3km scale. This enables the exploration of differences of the meteorological processes between the 11km and 3km scales, and the implications for urban areas and its surroundings. The overall study shows the potential of regional climate models to provide climate change information on urban scales.</p>


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