scholarly journals Evaluating Snow in EURO-CORDEX Regional Climate Models with Observations for the European Alps: Biases and Their Relationship to Orography, Temperature, and Precipitation Mismatches

Atmosphere ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 46
Author(s):  
Michael Matiu ◽  
Marcello Petitta ◽  
Claudia Notarnicola ◽  
Marc Zebisch
Keyword(s):  
Snow Cover ◽  
Snow Depth ◽  
Large Scale ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Horizontal Resolution ◽  
Winter Period ◽  
European Alps ◽  
Temperature And Precipitation

Climate models are important tools to assess current and future climate. While they have been extensively used for studying temperature and precipitation, only recently regional climate models (RCMs) arrived at horizontal resolutions that allow studies of snow in complex mountain terrain. Here, we present an evaluation of the snow variables in the World Climate Research Program Coordinated Regional Downscaling Experiment (EURO-CORDEX) RCMs with gridded observations of snow cover (from MODIS remote sensing) and temperature and precipitation (E-OBS), as well as with point (station) observations of snow depth and temperature for the European Alps. Large scale snow cover dynamics were reproduced well with some over- and under-estimations depending on month and RCM. The orography, temperature, and precipitation mismatches could on average explain 31% of the variability in snow cover bias across grid-cells, and even more than 50% in the winter period November–April. Biases in average monthly snow depth were remarkably low for reanalysis driven RCMs (<approx. 30 cm), and large for the GCM driven ones (up to 200 cm), when averaged over all stations within 400 m of altitude difference with RCM orography. Some RCMs indicated low snow cover biases and at the same time high snow depth biases, and vice versa. In summary, RCMs showed good skills in reproducing alpine snow cover conditions with regard to their limited horizontal resolution. Detected shortcomings in the models depended on the considered snow variable, season and individual RCM.

scholarly journals Bias correction and downscaling of snow cover fraction projections from regional climate models using remote sensing for the European Alps

2021 ◽  
Author(s):  
Michael Matiu ◽  
Florian Hanzer
Keyword(s):  
Remote Sensing ◽  
Snow Cover ◽  
Bias Correction ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
European Alps ◽  
Quantile Mapping ◽  
Snow Cover Fraction ◽  
Delta Change

Abstract. Mountain seasonal snow cover is undergoing major changes due to global climate change. Assessments of future snow cover usually rely on physical based models, and often include post-processed meteorology. Alternatively, here we propose a direct statistical adjustment of snow cover fraction from regional climate models by using long-term remote sensing observations. We compared different bias correction routines (delta change, quantile mapping, and quantile delta mapping) and explore a downscaling based on historical observations for the Greater Alpine Region in Europe. All bias correction methods adjust for systematic biases, for example due to topographic smoothing, and reduce model spread in future projections. Averaged over the study region and whole year, snow cover fraction decreases from 12.5 % in 2000–2020 to 10.4 (8.9, 11.5; model spread) % in 2071–2100 under RCP2.6, and 6.4 (4.1, 7.8) % under RCP8.5. In addition, changes strongly depended on season and altitude. The comparison of the statistical downscaling to a high-resolution physical based model yields similar results for the altitude range covered by the climate models, but different altitudinal gradients of change above and below. We found trend-preserving bias correction methods (delta change, quantile delta mapping) more plausible for snow cover fraction than quantile mapping. Downscaling showed potential but requires further research. Since climate model and remote sensing observations are available globally, the proposed methods are potentially widely applicable, but are limited to snow cover fraction only.

Biascorrected projections of snow cover fraction from EURO-CORDEX regional climate models with MODIS remote sensing observations for the European Alps

2020 ◽  
Author(s):  
Michael Matiu ◽  
Marcello Petitta ◽  
Claudia Notarnicola ◽  
Marc Zebisch
Keyword(s):  
Remote Sensing ◽  
Snow Cover ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Climate Projections ◽  
European Alps ◽  
Hydrological Models ◽  
Snow Cover Fraction

&lt;p&gt;Snow is a key environmental parameter in mountains, and in this changing climate reductions in snow are expected. Traditionally, future estimates of snow are based on dedicated snow/hydrological models forced by climate projections, which, however, are computationally intensive and which decouple hydrology from climate forcing. Recently, regional climate models (RCM) have been used as an alternative, although snow is only an auxiliary parameter in RCMs and not as accurately represented as compared to dedicated snow models. Nonetheless, RCMs encompass the climate-hydrology feedbacks, cover large areas, and have recently become available in moderate horizontal resolutions.&lt;/p&gt;&lt;p&gt;Here, we skip the need to biascorrect the input variables to the snow/hydrological models (i.e. temperature, precipitation, &amp;#8230;) and use observations to directly biascorrect the target variable, i.e. snow cover. Quantile delta mapping (QDM), a trend preserving bias correction method, is used to correct biases in EURO-CORDEX RCMs that provide snow cover fraction as output (CCLM4-8-17, ALADIN63, WRF331F, WRF381P, RACMO22E, RCA4) using remote sensing observations of snow cover from MODIS for the European Alps. As such, snow cover biases were accounted for, which originated mostly from orographic mismatches as well as temperature and precipitation biases. Model ensemble means were calculated for two emission scenarios (rcp26 and rcp85; with 6 and 21 GCM-RCM combinations available). The biascorrected projections can be used to put the climate model projections into context of current observations thus facilitating interpretations.&lt;/p&gt;&lt;p&gt;These are results from CliRSnow, a project that aims at providing bias corrected and downscaled projections of snow cover for the whole Alpine region until 2100. This project has received funding from the European Union&amp;#8217;s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 795310.&lt;/p&gt;

scholarly journals Improving runoff estimates from regional climate models: a performance analysis in Spain

2012 ◽  
Vol 16 (6) ◽  
pp. 1709-1723 ◽  
Author(s):  
D. González-Zeas ◽  
L. Garrote ◽  
A. Iglesias ◽  
A. Sordo-Ward
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Climate Models ◽  
Regional Climate ◽  
Aridity Index ◽  
Regional Climate Models ◽  
Hydrologic Model ◽  
Direct Runoff ◽  
Interpolation Methods ◽  
Basin Scale

Abstract. An important step to assess water availability is to have monthly time series representative of the current situation. In this context, a simple methodology is presented for application in large-scale studies in regions where a properly calibrated hydrologic model is not available, using the output variables simulated by regional climate models (RCMs) of the European project PRUDENCE under current climate conditions (period 1961–1990). The methodology compares different interpolation methods and alternatives to generate annual times series that minimise the bias with respect to observed values. The objective is to identify the best alternative to obtain bias-corrected, monthly runoff time series from the output of RCM simulations. This study uses information from 338 basins in Spain that cover the entire mainland territory and whose observed values of natural runoff have been estimated by the distributed hydrological model SIMPA. Four interpolation methods for downscaling runoff to the basin scale from 10 RCMs are compared with emphasis on the ability of each method to reproduce the observed behaviour of this variable. The alternatives consider the use of the direct runoff of the RCMs and the mean annual runoff calculated using five functional forms of the aridity index, defined as the ratio between potential evapotranspiration and precipitation. In addition, the comparison with respect to the global runoff reference of the UNH/GRDC dataset is evaluated, as a contrast of the "best estimator" of current runoff on a large scale. Results show that the bias is minimised using the direct original interpolation method and the best alternative for bias correction of the monthly direct runoff time series of RCMs is the UNH/GRDC dataset, although the formula proposed by Schreiber (1904) also gives good results.

scholarly journals Simulating future precipitation extremes in a complex Alpine catchment

2013 ◽  
Vol 13 (2) ◽  
pp. 263-277 ◽  
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.

scholarly journals Global Radiative Flux and Cloudiness Variability for the Period 1959–2010 in Belgium: A Comparison between Reanalyses and the Regional Climate Model MAR

Atmosphere ◽  
2018 ◽  
Vol 9 (7) ◽  
pp. 262 ◽  
Author(s):  
Coraline Wyard ◽  
Sébastien Doutreloup ◽  
Alexandre Belleflamme ◽  
Martin Wild ◽  
Xavier Fettweis
Keyword(s):  
Cloud Cover ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Horizontal Resolution ◽  
Radiative Flux ◽  
Added Value ◽  
Historical Trends ◽  
The Mean

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.

scholarly journals Regional Climate Model Emulator based on Deep Learning: Concept and First Evaluation of a Novel Hybrid Downscaling Approach

2021 ◽  
Author(s):  
Antoine Doury ◽  
Samuel Somot ◽  
Sébastien Gadat ◽  
Aurélien Ribes ◽  
Lola Corre
Keyword(s):  
Climate Change ◽  
Network Architecture ◽  
Large Scale ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Future Climate ◽  
Near Surface ◽  
The Way

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.

Deforestation impacts on Amazon-Andes hydroclimatic connectivity

2021 ◽  
Author(s):  
Juan Sierra ◽  
Jhan Carlo Espinoza ◽  
Clementine Junquas ◽  
Jan Polcher ◽  
Miguel Saavedra ◽  
...  
Keyword(s):  
South America ◽  
Transition Region ◽  
Amazon Basin ◽  
Climate Models ◽  
Regional Climate ◽  
Austral Summer ◽  
Regional Climate Models ◽  
Horizontal Resolution ◽  
Deforestation Rates ◽  
Tropical South America

&lt;p&gt;The Amazon rainforest is a key component of the climate system and one of the main planetary evapotranspiration sources. Over the entire Amazon basin, strong land-atmosphere feedbacks cause almost one third of the regional rainfall to be transpired by the local rainforest. Maximum precipitation recycling ratio takes place on the southwestern edge of the Amazon basin (a.k.a. Amazon-Andes transition region), an area recognized as the rainiest and biologically richest of the whole watershed. Here, high precipitation rates lead to large values of runoff per unit area providing most of the sediment load to Amazon rivers. As a consequence, the transition region can potentially be very sensitive to Amazonian forest loss. In fact, recent acceleration in deforestation rates has been reported over tropical South America. These sustained land-cover changes can alter the regional water and energy balances, as well as the regional circulation and rainfall patterns. In this sense, the use of regional climate models can help to understand the possible impacts of deforestation on the Amazon-Andes zone.&lt;/p&gt;&lt;p&gt;This work aims to assess the projected Amazonian deforestation effects on the moisture transport and rainfall behavior over tropical South America and the Amazon-Andes transition region. We perform 10-year austral summer simulations with the Weather Research and Forecasting model (WRF) using 3 one-way nested domains. Our finest domain is located over the south-western part of the basin, comprising two instrumented Andean Valleys (Zongo and Coroico river Valleys). Convective permitting high horizontal resolution (1km) is used over this domain. The outcomes presented here enhance the understanding of biosphere-atmosphere coupling and its deforestation induced disturbances.&lt;/p&gt;

scholarly journals The impact of RCM formulation and resolution on simulated precipitation in Africa

2019 ◽  
Author(s):  
Minchao Wu ◽  
Grigory Nikulin ◽  
Erik Kjellström ◽  
Danijel Belušić ◽  
Colin Jones ◽  
...  
Keyword(s):  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Horizontal Resolution ◽  
Added Value ◽  
Primary Control ◽  
Model Formulation ◽  
Simulated Precipitation ◽  
Mean Climate ◽  
The Impact

Abstract. We investigate the impact of model formulation and horizontal resolution on the ability of Regional Climate Models (RCMs) to simulate precipitation in Africa. Two RCMs – SMHI-RCA4 and HCLIM38-ALADIN are utilized for downscaling the ERA-Interim reanalysis over Africa at four different resolutions: 25, 50, 100 and 200 km. Additionally to the two RCMs, two different configurations of the same RCA4 are used. Contrasting different RCMs, configurations and resolutions it is found that model formulation has the primary control over many aspects of the precipitation climatology in Africa. Patterns of spatial biases in seasonal mean precipitation are mostly defined by model formulation while the magnitude of the biases is controlled by resolution. In a similar way, the phase of the diurnal cycle is completely controlled by model formulation (convection scheme) while its amplitude is a function of resolution. Although higher resolution in many cases leads to smaller biases in the time mean climate, the impact of higher resolution is mixed. An improvement in one region/season (e.g. reduction of dry biases) often corresponds to a deterioration in another region/season (e.g. amplification of wet biases). The experiments confirm a pronounced and well known impact of higher resolution – a more realistic distribution of daily precipitation. Even if the time-mean climate is not always greatly sensitive to resolution, what the time-mean climate is made up of, higher order statistics, is sensitive. Therefore, the realism of the simulated precipitation increases as resolution increases. Our results show that improvements in the ability of RCMs to simulate precipitation in Africa compared to their driving reanalysis in many cases are simply related to model formulation and not necessarily to higher resolution. Such model formulation related improvements are strongly model dependent and in general cannot be considered as an added value of downscaling.

scholarly journals Surface accumulation in Northern Central Greenland during the last 300 years

2020 ◽  
Vol 61 (81) ◽  
pp. 214-224 ◽  
Author(s):  
Nanna B. Karlsson ◽  
Sebastian Razik ◽  
Maria Hörhold ◽  
Anna Winter ◽  
Daniel Steinhage ◽  
...  
Keyword(s):  
Large Scale ◽  
Climate Models ◽  
Regional Climate ◽  
Ice Core ◽  
Regional Climate Models ◽  
Radar Data ◽  
Accumulation Rates ◽  
Ground Penetrating ◽  
Core Project ◽  
North Greenland

AbstractThe internal stratigraphy of snow and ice as imaged by ground-penetrating radar may serve as a source of information on past accumulation. This study presents results from two ground-based radar surveys conducted in Greenland in 2007 and 2015, respectively. The first survey was conducted during the traverse from the ice-core station NGRIP (North Greenland Ice Core Project) to the ice-core station NEEM (North Greenland Eemian Ice Drilling). The second survey was carried out during the traverse from NEEM to the ice-core station EGRIP (East Greenland Ice Core Project) and then onwards to Summit Station. The total length of the radar profiles is 1427 km. From the radar data, we retrieve the large-scale spatial variation of the accumulation rates in the interior of the ice sheet. The accumulation rates range from 0.11 to 0.26 m a−1 ice equivalent with the lowest values found in the northeastern sector towards EGRIP. We find no evidence of temporal or spatial changes in accumulation rates when comparing the 150-year average accumulation rates with the 321-year average accumulation rates. Comparisons with regional climate models reveal that the models underestimate accumulation rates by up to 35% in northeastern Greenland. Our results serve as a robust baseline to detect present changes in either surface accumulation rates or patterns.

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