scholarly journals Precipitation Climatology in an Ensemble of CORDEX-Africa Regional Climate Simulations

2012 ◽  
Vol 25 (18) ◽  
pp. 6057-6078 ◽  
Author(s):  
Grigory Nikulin ◽  
Colin Jones ◽  
Filippo Giorgi ◽  
Ghassem Asrar ◽  
Matthias Büchner ◽  
...  
Keyword(s):  
Diurnal Cycle ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Small Subset ◽  
Climate Projections ◽  
Systematic Bias ◽  
Diurnal Cycles ◽  
Climate Simulations ◽  
Simulated Precipitation

Abstract An ensemble of regional climate simulations is analyzed to evaluate the ability of 10 regional climate models (RCMs) and their ensemble average to simulate precipitation over Africa. All RCMs use a similar domain and spatial resolution of ~50 km and are driven by the ECMWF Interim Re-Analysis (ERA-Interim) (1989–2008). They constitute the first set of simulations in the Coordinated Regional Downscaling Experiment in Africa (CORDEX-Africa) project. Simulated precipitation is evaluated at a range of time scales, including seasonal means, and annual and diurnal cycles, against a number of detailed observational datasets. All RCMs simulate the seasonal mean and annual cycle quite accurately, although individual models can exhibit significant biases in some subregions and seasons. The multimodel average generally outperforms any individual simulation, showing biases of similar magnitude to differences across a number of observational datasets. Moreover, many of the RCMs significantly improve the precipitation climate compared to that from their boundary condition dataset, that is, ERA-Interim. A common problem in the majority of the RCMs is that precipitation is triggered too early during the diurnal cycle, although a small subset of models does have a reasonable representation of the phase of the diurnal cycle. The systematic bias in the diurnal cycle is not improved when the ensemble mean is considered. Based on this performance analysis, it is assessed that the present set of RCMs can be used to provide useful information on climate projections over Africa.

Regional Climate Scenarios for use in Nordic Water Resources Studies

2003 ◽  
Vol 34 (5) ◽  
pp. 399-412 ◽  
Author(s):  
M. Rummukainen ◽  
J. Räisänen ◽  
D. Bjørge ◽  
J.H. Christensen ◽  
O.B. Christensen ◽  
...  
Keyword(s):  
Water Resources ◽  
Climate Models ◽  
Global Climate ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Climate Projections ◽  
Climate Scenarios ◽  
Soil Frost ◽  
Nordic Region ◽  
Regional Climate Projections

According to global climate projections, a substantial global climate change will occur during the next decades, under the assumption of continuous anthropogenic climate forcing. Global models, although fundamental in simulating the response of the climate system to anthropogenic forcing are typically geographically too coarse to well represent many regional or local features. In the Nordic region, climate studies are conducted in each of the Nordic countries to prepare regional climate projections with more detail than in global ones. Results so far indicate larger temperature changes in the Nordic region than in the global mean, regional increases and decreases in net precipitation, longer growing season, shorter snow season etc. These in turn affect runoff, snowpack, groundwater, soil frost and moisture, and thus hydropower production potential, flooding risks etc. Regional climate models do not yet fully incorporate hydrology. Water resources studies are carried out off-line using hydrological models. This requires archived meteorological output from climate models. This paper discusses Nordic regional climate scenarios for use in regional water resources studies. Potential end-users of water resources scenarios are the hydropower industry, dam safety instances and planners of other lasting infrastructure exposed to precipitation, river flows and flooding.

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

2019 ◽  
Vol 58 (12) ◽  
pp. 2617-2632 ◽  
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.

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.

Future humidity extremes in an urban-rural context - using regional climate model projections down to convection permitting scales for Berlin

2021 ◽  
Author(s):  
Gaby S. Langendijk ◽  
Diana Rechid ◽  
Daniela Jacob
Keyword(s):  
Climate Change ◽  
Urban Areas ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Climate Change Impacts ◽  
Regional Climate Models ◽  
Climate Projections ◽  
Climate Data ◽  
Urban Rural

<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>

Performance evaluation of bias adjustment methods for wave climate projections in the Mediterranean Sea

2021 ◽  
Author(s):  
Andrea Lira Loarca ◽  
Giovanni Besio
Keyword(s):  
Bias Correction ◽  
Temporal Variability ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Wave Climate ◽  
Mapping Method ◽  
Climate Projections ◽  
Bias Adjustment ◽  
Distribution Mapping

<p>Global and regional climate models are the primary tools to investigate the climate system response to different scenarios and therefore allow to make future projections of different atmospheric variables which are used as input for wave generation models to assess future wave climate. Adequate projections of future wave climate are needed in order to analyze climate change impacts and hazards in coastal areas such as flooding and erosion with waves being the predominant factor with varied temporal variability. </p><p>Bias adjustment methods are commonly used for climate impact variables dealing with systematic errors (biases) found in global and regional climate models.  While bias correction techniques are extended in the climate and hydrological impact modeling scientific communities, there is still a lack of consensus regarding their use in sea climate variables (Parker & Hill, 2017; Lemos et al, 2020; Lira-Loarca et at, 2021)</p><p>In these work we assess the performance of different bias-adjustment methods such as the Empirical Gumbel Quantile Mapping (EGQM) method as a standard method which takes into the account the extreme values of the distribution takes, the Distribution Mapping method using Stationary Mixture Distributions (DM-stMix) allowing for a better representation of each variable in the mean regime and tails and the Distribution Mapping method using Non-Stationary Mixture Distributions (DM-nonstMix) as an improved methods which allows to take into account the temporal variability of wave climate according to different baseline periods such as monthly, seasonal, yearly and decadal. The performance of the different bias adjustment methods will be analyzed with particular interest on the futural temporal behavior of wave climate. The advantages and drawbacks of each bias adjustment method as well as their complexity will be discussed.</p><p> </p><p><em>References:</em></p><ul><li>Lemos, G., Menendez, M., Semedo, A., Camus, P., Hemer, M., Dobrynin, M., Miranda, P.M.A. (2020). On the need of bias correction methods for wave climate projections, Global and Planetary Change, 186, 103109.</li> <li><span>Lira-Loarca, A., Cobos, M., Besio, G., Baquerizo, A. (2021) Projected wave climate temporal variability due to climate change. Stoch Environ Res Risk Assess.</span></li> <li><span>Parker, K. & Hill, D.F. (2017) Evaluation of bias correction methods for wave modeling output, Ocean Modelling 110, 52-65</span></li> </ul><p><br><br></p>

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 Influence of Bias Correction Methods on Simulated Köppen−Geiger Climate Zones in Europe

Climate ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 18 ◽  
Author(s):  
Beáta Szabó-Takács ◽  
Aleš Farda ◽  
Petr Skalák ◽  
Jan Meitner
Keyword(s):  
Bias Correction ◽  
Climate Models ◽  
Regional Climate ◽  
Generalized Pareto Distribution ◽  
Regional Climate Models ◽  
Climate Classification ◽  
Quantile Mapping ◽  
Temperature Bias ◽  
Simulated Precipitation ◽  
Precipitation And Temperature

Our goal was to investigate the influence of bias correction methods on climate simulations over the European domain. We calculated the Köppen−Geiger climate classification using five individual regional climate models (RCM) of the ENSEMBLES project in the European domain during the period 1961−1990. The simulated precipitation and temperature data were corrected using the European daily high-resolution gridded dataset (E-OBS) observed data by five methods: (i) the empirical quantile mapping of precipitation and temperature, (ii) the quantile mapping of precipitation and temperature based on gamma and Generalized Pareto Distribution of precipitation, (iii) local intensity scaling, (iv) the power transformation of precipitation and (v) the variance scaling of temperature bias corrections. The individual bias correction methods had a significant effect on the climate classification, but the degree of this effect varied among the RCMs. Our results on the performance of bias correction differ from previous results described in the literature where these corrections were implemented over river catchments. We conclude that the effect of bias correction may depend on the region of model domain. These results suggest that distribution free bias correction approaches are the most suitable for large domain sizes such as the pan-European domain.

scholarly journals A Diagnostic Evaluation of Precipitation in CORDEX Models over Southern Africa

2013 ◽  
Vol 26 (23) ◽  
pp. 9477-9506 ◽  
Author(s):  
Evangelia-Anna Kalognomou ◽  
Christopher Lennard ◽  
Mxolisi Shongwe ◽  
Izidine Pinto ◽  
Alice Favre ◽  
...  
Keyword(s):  
Southern Africa ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Precipitation Anomaly ◽  
Ensemble Average ◽  
Climate Projections ◽  
Time Model ◽  
Domain Models ◽  
Model Setup

The authors evaluate the ability of 10 regional climate models (RCMs) to simulate precipitation over Southern Africa within the Coordinated Regional Climate Downscaling Experiment (CORDEX) framework. An ensemble of 10 regional climate simulations and the ensemble average is analyzed to evaluate the models' ability to reproduce seasonal and interannual regional climatic features over regions of the subcontinent. All the RCMs use a similar domain, have a spatial resolution of ~50 km, and are driven by the Interim ECMWF Re-Analysis (ERA-Interim; 1989–2008). Results are compared against a number of observational datasets.In general, the spatial and temporal nature of rainfall over the region is captured by all RCMs, although individual models exhibit wet or dry biases over particular regions of the domain. Models generally produce lower seasonal variability of precipitation compared to observations and the magnitude of the variability varies in space and time. Model biases are related to model setup, simulated circulation anomalies, and moisture transport. The multimodel ensemble mean generally outperforms individual models, with bias magnitudes similar to differences across the observational datasets. In the northern parts of the domain, some of the RCMs and the ensemble average improve the precipitation climate compared to that of ERA-Interim. The models are generally able to capture the dry (wet) precipitation anomaly associated with El Niño (La Niña) events across the region. Based on this analysis, the authors suggest that the present set of RCMs can be used to provide useful information on climate projections of rainfall over Southern Africa.

scholarly journals PREDICTION OF SHORELINE CHANGES FOR DIFFERENT COASTAL CONFIGURATIONS USING FUTURE CLIMATE PROJECTIONS

2018 ◽  
pp. 82
Author(s):  
B R Rajasree ◽  
M C Deo
Keyword(s):  
Sediment Transport ◽  
Climate Models ◽  
Numerical Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Shoreline Change ◽  
Climate Projections ◽  
Wave Data ◽  
Different Types ◽  
And Behavior

The estimation of shoreline change as well as sediment transport at a specified site can be reliably made with the help of corresponding numerical models that are run with the help of historical wave data generated using windwave models based on the input of past wind conditions. It is well known that the magnitude and behavior of historical wind and waves would not remain same in future as a result of the climate change induced by global warming. (Komar et al., 2010). In this light the present study attempts to understand what happens if future wind and waves are generated using regional climate models (RCMs) and the shoreline change and sediment transport is determined on that basis instead of historical wind and wave data. Toward this we have considered there different types of shorelines, namely (a) an uninterrupted coastal stretch, (b) the coast interrupted by an artificial structure and (c) the coast interrupted by natural features. This study goes beyond an earlier one (Rajasree et al., 2016) in which only case (a) was discussed and where coastal vulnerability was not assessed.

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