scholarly journals Assessing the climate change impact on the North African offshore surface wind and coastal low-level jet using coupled and uncoupled regional climate simulations

2018 ◽  
Vol 52 (11) ◽  
pp. 7111-7132 ◽  
Author(s):  
Pedro M. M. Soares ◽  
Daniela C. A. Lima ◽  
Alvaro Semedo ◽  
Rita M. Cardoso ◽  
William Cabos ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Change Impact ◽  
Regional Climate ◽  
Surface Wind ◽  
North African ◽  
Low Level ◽  
The North ◽  
Climate Simulations ◽  
Change Impact ◽  
Low Level Jet

Cluster analysis of the ensembles of EURO-CORDEX simulations

2021 ◽  
Author(s):  
Tímea Kalmár ◽  
Rita Pongrácz ◽  
Ildikó Pieczka
Keyword(s):  
Climate Change ◽  
Cluster Analysis ◽  
Climate Change Impact ◽  
Climate Models ◽  
Regional Climate ◽  
Climate Change Research ◽  
Model Simulations ◽  
Climate Simulations ◽  
Change Impact ◽  
Precipitation And Temperature

<p>Climate models play an important role in global and regional climate change research, improving our understanding and predictability of climate behaviour. The CORDEX (Coordinated Regional Downscaling Experiment) program was established to provide a framework for the assessment of Regional Climate Models (RCMs) and to contribute to climate change impact assessment and adaptation processes. The climate simulations are based on multiple dynamical and empirical-statistical downscaling models forced by multiple global climate models (GCMs). The motivation behind the use of multiple models in climate change research is to cover different sources of uncertainties, that is why it is recommended to use all available simulations in climate change studies. However, many climate change impact studies face difficulties (e.g., limited computing resources or free access to climate data) using all the available simulations, and therefore it is quite often the case that only subsets of simulations are used. Another problem is that the ensembles of GCM-RCM simulations are too big to be handled by many impact modellers. The selection of model simulations is subjective in most cases, and it is often reduced by hand-picking climate simulations depending on the partners involved in the project. An objective method can be based on cluster analysis, which is a flexible and unsupervised numerical technique that involves the sorting of data into statistically similar groups. These groups can be either (i) determined entirely by the properties of the data themselves or (ii) guided by user constraints. In the present study, we focus on Central-Eastern Europe, because the model simulations are particularly uncertain in the precipitation and temperature distribution over this region. The aim of the study is to develop a method based on the precipitation and temperature values of 55 EURO-CORDEX simulations for a near-present historical period (1995–2014), which could help to select suitable subsets of ensembles of climate simulations tailored to the needs within climate change impact studies.</p><p> </p><p>Acknowledgement: This study is supported by the ÚNKP-20-3 New National Excellence Program of the Ministry for Innovation and Technology from the source of the National Research, Development and Innovation Fund.</p>

scholarly journals Fine-resolution climate projections enhance regional climate change impact studies

Eos ◽  
2007 ◽  
Vol 88 (47) ◽  
pp. 504-504 ◽  
Author(s):  
Edwin P. Maurer ◽  
Levi Brekke ◽  
Tom Pruitt ◽  
Philip B. Duffy
Keyword(s):  
Climate Change ◽  
Climate Change Impact ◽  
Regional Climate ◽  
Regional Climate Change ◽  
Climate Projections ◽  
Impact Studies ◽  
Change Impact ◽  
Fine Resolution

scholarly journals Toward Assessing NARCCAP Regional Climate Model Credibility for the North American Monsoon: Future Climate Simulations*

2015 ◽  
Vol 28 (17) ◽  
pp. 6707-6728 ◽  
Author(s):  
Melissa S. Bukovsky ◽  
Carlos M. Carrillo ◽  
David J. Gochis ◽  
Dorit M. Hammerling ◽  
Rachel R. McCrary ◽  
...  
Keyword(s):  
Climate Change ◽  
North American ◽  
Southern Oscillation ◽  
Monsoon Season ◽  
Regional Climate ◽  
Global Climate Models ◽  
North American Monsoon ◽  
The North ◽  
Climate Simulations ◽  
Projected Changes

Abstract This study presents climate change results from the North American Regional Climate Change Assessment Program (NARCCAP) suite of dynamically downscaled simulations for the North American monsoon system in the southwestern United States and northwestern Mexico. The focus is on changes in precipitation and the processes driving the projected changes from the regional climate simulations and their driving coupled atmosphere–ocean global climate models. The effect of known biases on the projections is also examined. Overall, there is strong ensemble agreement for a large decrease in precipitation during the monsoon season; however, this agreement and the magnitude of the ensemble-mean change is likely deceiving, as the greatest decreases are produced by the simulations that are the most biased in the baseline/current climate. Furthermore, some of the greatest decreases in precipitation are being driven by changes in processes/phenomena that are less credible (e.g., changes in El Niño–Southern Oscillation, when it is initially not simulated well). In other simulations, the processes driving the precipitation change may be plausible, but other biases (e.g., biases in low-level moisture or precipitation intensity) appear to be affecting the magnitude of the projected changes. The most and least credible simulations are clearly identified, while the other simulations are mixed in their abilities to produce projections of value.

scholarly journals Is bias correction of Regional Climate Model (RCM) simulations possible for non-stationary conditions?

2012 ◽  
Vol 9 (11) ◽  
pp. 12765-12795 ◽  
Author(s):  
C. Teutschbein ◽  
J. Seibert
Keyword(s):  
Climate Change ◽  
Bias Correction ◽  
Climate Change Impact ◽  
Climate Model ◽  
Regional Climate ◽  
Climate Conditions ◽  
Impact Studies ◽  
Split Sample ◽  
Sample Test ◽  
Change Impact

Abstract. In hydrological climate-change impact studies, Regional Climate Models (RCMs) are commonly used to transfer large-scale Global Climate Model (GCM) data to smaller scales and to provide more detailed regional information. However, there are often considerable biases in RCM simulations, which have led to the development of a number of bias correction approaches to provide more realistic climate simulations for impact studies. Bias correction procedures rely on the assumption that RCM biases do not change over time, because correction algorithms and their parameterizations are derived for current climate conditions and assumed to apply also for future climate conditions. This underlying assumption of bias stationarity is the main concern when using bias correction procedures. It is in principle not possible to test whether this assumption is actually fulfilled for future climate conditions. In this study, however, we demonstrate that it is possible to evaluate how well bias correction methods perform for conditions different from those used for calibration. For five Swedish catchments, several time series of RCM simulated precipitation and temperature were obtained from the ENSEMBLES data base and different commonly-used bias correction methods were applied. We then performed a differential split-sample test by dividing the data series into cold and warm respective dry and wet years. This enabled us to evaluate the performance of different bias correction procedures under systematically varying climate conditions. The differential split-sample test resulted in a large spread and a clear bias for some of the correction methods during validation years. More advanced correction methods such as distribution mapping performed relatively well even in the validation period, whereas simpler approaches resulted in the largest deviations and least reliable corrections for changed conditions. Therefore, we question the use of simple bias correction methods such as the widely used delta-change approach and linear scaling for RCM-based climate-change impact studies and recommend using higher-skill bias correction methods.

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