Projected increases in summer and winter UK sub-daily precipitation extremes from high-resolution regional climate models

Downscaling data from General Circulation Models (GCMs) with Regional Climate Models (RCMs) is a computationally expensive process, even more so running at the convection permitting scale (CP). Despite the high-resolution products of these simulations, the Added Value (AV) of these runs compared to their driving models is an important factor for consideration. A new method was recently developed to quantify the AV of historical simulations as well as the Climate Change Downscaling Signal (CCDS) of forecast runs. This method presents these quantities spatially and thus the specific regions with the most AV can be identified and understood.An analysis of daily precipitation from a 55-model EURO-CORDEX ensemble (at 12 km resolution) was assessed using this method. It revealed positive AV throughout the domain with greater emphasis in regions of complex topography, coast-lines, and the tropics. Similar CCDS was obtained when assessing the RCP 8.5 far future runs in these domains. This paper looks more closely at the CCDS obtained with this method and compares it to other climate change signals described in other studies.The same method is now being applied to assess the AV and CCDS of daily precipitation from an ensemble of models at the CP scale (~3 km) over different domains within Europe. The current stage of the analysis is also looking into the AV of using hourly precipitation instead of daily.

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Antarctic ice shelf thickness change from multimission lidar mapping

The Cryosphere ◽

10.5194/tc-13-1801-2019 ◽

2019 ◽

Vol 13 (7) ◽

pp. 1801-1817 ◽

Cited By ~ 2

Author(s):

Tyler C. Sutterley ◽

Thorsten Markus ◽

Thomas A. Neumann ◽

Michiel van den Broeke ◽

J. Melchior van Wessem ◽

...

Keyword(s):

High Resolution ◽

Climate Models ◽

Regional Climate ◽

Regional Climate Models ◽

Surface Velocity ◽

Ice Thickness ◽

Flux Divergence ◽

Ice Shelf ◽

Thickness Change ◽

Ice Shelves

Abstract. We calculate rates of ice thickness change and bottom melt for ice shelves in West Antarctica and the Antarctic Peninsula from a combination of elevation measurements from NASA–CECS Antarctic ice mapping campaigns and NASA Operation IceBridge corrected for oceanic processes from measurements and models, surface velocity measurements from synthetic aperture radar, and high-resolution outputs from regional climate models. The ice thickness change rates are calculated in a Lagrangian reference frame to reduce the effects from advection of sharp vertical features, such as cracks and crevasses, that can saturate Eulerian-derived estimates. We use our method over different ice shelves in Antarctica, which vary in terms of size, repeat coverage from airborne altimetry, and dominant processes governing their recent changes. We find that the Larsen-C Ice Shelf is close to steady state over our observation period with spatial variations in ice thickness largely due to the flux divergence of the shelf. Firn and surface processes are responsible for some short-term variability in ice thickness of the Larsen-C Ice Shelf over the time period. The Wilkins Ice Shelf is sensitive to short-timescale coastal and upper-ocean processes, and basal melt is the dominant contributor to the ice thickness change over the period. At the Pine Island Ice Shelf in the critical region near the grounding zone, we find that ice shelf thickness change rates exceed 40 m yr−1, with the change dominated by strong submarine melting. Regions near the grounding zones of the Dotson and Crosson ice shelves are decreasing in thickness at rates greater than 40 m yr−1, also due to intense basal melt. NASA–CECS Antarctic ice mapping and NASA Operation IceBridge campaigns provide validation datasets for floating ice shelves at moderately high resolution when coregistered using Lagrangian methods.

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New Approach for Bias Correction and Stochastic Downscaling of Future Projections for Daily Mean Temperatures to a High-Resolution Grid

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-19-0086.1 ◽

2019 ◽

Vol 58 (12) ◽

pp. 2617-2632 ◽

Cited By ~ 1

Author(s):

Qifen Yuan ◽

Thordis L. Thorarinsdottir ◽

Stein Beldring ◽

Wai Kwok Wong ◽

Shaochun Huang ◽

...

Keyword(s):

High Resolution ◽

Bias Correction ◽

Climate Models ◽

Regional Climate ◽

Regional Climate Models ◽

Climate Change Signal ◽

Climate Projections ◽

Data Product ◽

Temporal Features ◽

Stochastic Downscaling

AbstractIn applications of climate information, coarse-resolution climate projections commonly need to be downscaled to a finer grid. One challenge of this requirement is the modeling of subgrid variability and the spatial and temporal dependence at the finer scale. Here, a postprocessing procedure for temperature projections is proposed that addresses this challenge. The procedure employs statistical bias correction and stochastic downscaling in two steps. In the first step, errors that are related to spatial and temporal features of the first two moments of the temperature distribution at model scale are identified and corrected. Second, residual space–time dependence at the finer scale is analyzed using a statistical model, from which realizations are generated and then combined with an appropriate climate change signal to form the downscaled projection fields. Using a high-resolution observational gridded data product, the proposed approach is applied in a case study in which projections of two regional climate models from the Coordinated Downscaling Experiment–European Domain (EURO-CORDEX) ensemble are bias corrected and downscaled to a 1 km × 1 km grid in the Trøndelag area of Norway. A cross-validation study shows that the proposed procedure generates results that better reflect the marginal distributional properties of the data product and have better consistency in space and time when compared with empirical quantile mapping.

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Evaluation of uncertainties in mean and extreme precipitation under climate change for northwestern Mediterranean watersheds from high-resolution Med and Euro-CORDEX ensembles

Hydrology and Earth System Sciences ◽

10.5194/hess-22-673-2018 ◽

2018 ◽

Vol 22 (1) ◽

pp. 673-687 ◽

Cited By ~ 10

Author(s):

Antoine Colmet-Daage ◽

Emilia Sanchez-Gomez ◽

Sophie Ricci ◽

Cécile Llovel ◽

Valérie Borrell Estupina ◽

...

Keyword(s):

Climate Change ◽

High Resolution ◽

Extreme Precipitation ◽

Climate Models ◽

Regional Climate ◽

Regional Climate Models ◽

Historical Period ◽

Mountainous Regions ◽

Extreme Precipitation Events ◽

Precipitation Events

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.

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Simulating future precipitation extremes in a complex Alpine catchment

Natural Hazards and Earth System Science ◽

10.5194/nhess-13-263-2013 ◽

2013 ◽

Vol 13 (2) ◽

pp. 263-277 ◽

Cited By ~ 9

Author(s):

C. Dobler ◽

G. Bürger ◽

J. Stötter

Keyword(s):

Climate Change ◽

Large Scale ◽

Climate Models ◽

Regional Climate ◽

Regional Climate Models ◽

Precipitation Extremes ◽

Climate Projections ◽

Research Station ◽

Climate Change Scenarios ◽

Station Data

Abstract. The objectives of the present investigation are (i) to study the effects of climate change on precipitation extremes and (ii) to assess the uncertainty in the climate projections. The investigation is performed on the Lech catchment, located in the Northern Limestone Alps. In order to estimate the uncertainty in the climate projections, two statistical downscaling models as well as a number of global and regional climate models were considered. The downscaling models applied are the Expanded Downscaling (XDS) technique and the Long Ashton Research Station Weather Generator (LARS-WG). The XDS model, which is driven by analyzed or simulated large-scale synoptic fields, has been calibrated using ECMWF-interim reanalysis data and local station data. LARS-WG is controlled through stochastic parameters representing local precipitation variability, which are calibrated from station data only. Changes in precipitation mean and variability as simulated by climate models were then used to perturb the parameters of LARS-WG in order to generate climate change scenarios. In our study we use climate simulations based on the A1B emission scenario. The results show that both downscaling models perform well in reproducing observed precipitation extremes. In general, the results demonstrate that the projections are highly variable. The choice of both the GCM and the downscaling method are found to be essential sources of uncertainty. For spring and autumn, a slight tendency toward an increase in the intensity of future precipitation extremes is obtained, as a number of simulations show statistically significant increases in the intensity of 90th and 99th percentiles of precipitation on wet days as well as the 5- and 20-yr return values.

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Analysis of precipitation extremes with the assessment of regional climate models over the Willamette River Basin, USA

Hydrological Processes ◽

10.1002/hyp.9376 ◽

2012 ◽

Vol 27 (18) ◽

pp. 2579-2590 ◽

Cited By ~ 33

Author(s):

Andrew Halmstad ◽

Mohammad Reza Najafi ◽

Hamid Moradkhani

Keyword(s):

River Basin ◽

Climate Models ◽

Regional Climate ◽

Regional Climate Models ◽

Precipitation Extremes ◽

Willamette River ◽

Willamette River Basin

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Using high-resolution regional climate models to estimate return levels of daily extreme precipitation over Bavaria

Natural Hazards and Earth System Science ◽

10.5194/nhess-21-3573-2021 ◽

2021 ◽

Vol 21 (11) ◽

pp. 3573-3598

Author(s):

Benjamin Poschlod

Keyword(s):

High Resolution ◽

Climate Models ◽

Regional Climate ◽

Daily Rainfall ◽

Regional Climate Models ◽

Reanalysis Data ◽

Extreme Value ◽

Return Level ◽

Gev Distribution ◽

Return Levels

Abstract. Extreme daily rainfall is an important trigger for floods in Bavaria. The dimensioning of water management structures as well as building codes is based on observational rainfall return levels. In this study, three high-resolution regional climate models (RCMs) are employed to produce 10- and 100-year daily rainfall return levels and their performance is evaluated by comparison to observational return levels. The study area is governed by different types of precipitation (stratiform, orographic, convectional) and a complex terrain, with convective precipitation also contributing to daily rainfall levels. The Canadian Regional Climate Model version 5 (CRCM5) at a 12 km spatial resolution and the Weather and Forecasting Research (WRF) model at a 5 km resolution both driven by ERA-Interim reanalysis data use parametrization schemes to simulate convection. WRF at a 1.5 km resolution driven by ERA5 reanalysis data explicitly resolves convectional processes. Applying the generalized extreme value (GEV) distribution, the CRCM5 setup can reproduce the observational 10-year return levels with an areal average bias of +6.6 % and a spatial Spearman rank correlation of ρ=0.72. The higher-resolution 5 km WRF setup is found to improve the performance in terms of bias (+4.7 %) and spatial correlation (ρ=0.82). However, the finer topographic details of the WRF-ERA5 return levels cannot be evaluated with the observation data because their spatial resolution is too low. Hence, this comparison shows no further improvement in the spatial correlation (ρ=0.82) but a small improvement in the bias (2.7 %) compared to the 5 km resolution setup. Uncertainties due to extreme value theory are explored by employing three further approaches. Applied to the WRF-ERA5 data, the GEV distributions with a fixed shape parameter (bias is +2.5 %; ρ=0.79) and the generalized Pareto (GP) distributions (bias is +2.9 %; ρ=0.81) show almost equivalent results for the 10-year return period, whereas the metastatistical extreme value (MEV) distribution leads to a slight underestimation (bias is −7.8 %; ρ=0.84). For the 100-year return level, however, the MEV distribution (bias is +2.7 %; ρ=0.73) outperforms the GEV distribution (bias is +13.3 %; ρ=0.66), the GEV distribution with fixed shape parameter (bias is +12.9 %; ρ=0.70), and the GP distribution (bias is +11.9 %; ρ=0.63). Hence, for applications where the return period is extrapolated, the MEV framework is recommended. From these results, it follows that high-resolution regional climate models are suitable for generating spatially homogeneous rainfall return level products. In regions with a sparse rain gauge density or low spatial representativeness of the stations due to complex topography, RCMs can support the observational data. Further, RCMs driven by global climate models with emission scenarios can project climate-change-induced alterations in rainfall return levels at regional to local scales. This can allow adjustment of structural design and, therefore, adaption to future precipitation conditions.

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