scholarly journals Variations in the Simulation of Climate Change Impact Indices due to Different Land Surface Schemes over the Mediterranean, Middle East and Northern Africa

Atmosphere ◽  
2019 ◽  
Vol 10 (1) ◽  
pp. 26 ◽  
Author(s):  
Katiana Constantinidou ◽  
George Zittis ◽  
Panos Hadjinicolaou
Keyword(s):  
Climate Change ◽  
Middle East ◽  
Land Surface ◽  
Climate Change Impact ◽  
Climate Model ◽  
Eastern Mediterranean ◽  
Regional Climate ◽  
Climatic Conditions ◽  
Relative Dispersion ◽  
Change Impact

The Eastern Mediterranean (EM) and the Middle East and North Africa (MENA) are projected to be exposed to extreme climatic conditions in the 21st century, which will likely induce adverse impacts in various sectors. Relevant climate change impact assessments utilise data from climate model projections and process-based impact models or simpler, index-based approaches. In this study, we explore the implied uncertainty from variations of climate change impact-related indices as induced by the modelled climate (WRF regional climate model) from different land surface schemes (Noah, NoahMP, CLM and RUC). The three climate change impact-related indicators examined here are the Radiative Index of Dryness (RID), the Fuel Dryness Index (Fd) and the Water-limited Yield (Yw). Our findings indicate that Noah simulates the highest values for both RID and Fd, while CLM gives the highest estimations for winter wheat Yw. The relative dispersion in the three indices derived by the different land schemes is not negligible, amounting, for the overall geographical domain of 25% for RID and Fd, and 10% for Yw. The dispersion is even larger for specific sub-regions.

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.

scholarly journals Modeling irrigation effects on the regional climate in the "Greater Alpine Region" using a newly developed parameterization

2021 ◽  
Author(s):  
Christina Asmus ◽  
Peter Hoffmann ◽  
Joni-Pekka Pietikäinen ◽  
Jürgen Böhner ◽  
Diana Rechid
Keyword(s):  
Climate Change ◽  
Land Surface ◽  
Climate Model ◽  
Regional Climate ◽  
Regional Climate Change ◽  
Climatic Conditions ◽  
Alpine Region ◽  
Mitigation Potential ◽  
Po Valley ◽  
Irrigation Effects

<p><span>Irrigation is a common </span><span>land use </span><span>practice to adapt agriculture to unsuitable climatic conditions. It is highly relevant to ensure food production. Due to the growing population and its food demand in the future, as well as due to climate change, the irrigated area</span><span>s</span> <span>are</span><span> expected to increase </span><span>globally</span><span>. Therefore, it is important to understand the effects of irrigation on the climate system. Irrigation of cropland alters the biogeophysical properties of the land surface and the soil. Due to the land-atmosphere interactions, these alterations </span><span>have the potential to</span><span> affect the atmosphere directly or through feedback processes. Various studies point out that the effects of irrigation, like temperature reduction, are particularly pronounced on local to regional scales where they bear a mitigation potential to regional climate change. </span></p><p><span>This study aims to investigate the effects of irrigation on the regional climate. To model these effects, we developed and implemented a new flexible irrigation parameterization into the regional climate model REMO. In our setup, REMO is interactively coupled to the mosaic-based vegetation module iMOVE, enabling the calculation of irrigation effects and feedbacks on land, vegetation, and atmosphere. Multiple simulations for specific climatic conditions with </span><span>and without </span><span>the </span><span>new</span><span> irrigation parameterization are conducted on 0.11° resolution for the ”Greater Alpine Region“, which includes some of Europe‘s most intensively irrigated areas like the Po valley in Northern Italy. The differences between these simulations are analyzed to identify and quantify irrigation effects on atmospheric processes. </span></p><p><span>The </span><span>new irrigation parameterization will be introduced and the</span><span> analysis </span><span>of the irrigation effects</span> <span>on the regional climate in the “Greater Alpine Region” </span><span>will be presented. </span></p>

scholarly journals Assessment of climate change impact on the malaria vector Anopheles hyrcanus, West Nile disease, and incidence of melanoma in the Vojvodina Province (Serbia) using data from a regional climate model

PLoS ONE ◽  
2020 ◽  
Vol 15 (1) ◽  
pp. e0227679
Author(s):  
Dragutin T. Mihailović ◽  
Dušan Petrić ◽  
Tamaš Petrović ◽  
Ivana Hrnjaković-Cvjetković ◽  
Vladimir Djurdjevic ◽  
...  
Keyword(s):  
Climate Change ◽  
Regional Climate Model ◽  
Malaria Vector ◽  
Climate Change Impact ◽  
Climate Model ◽  
Regional Climate ◽  
West Nile ◽  
Change Impact ◽  
Using Data ◽  
Vojvodina Province

Climate change impact on nutrient loss in regions with pronounced winter seasons

2010 ◽  
Vol 1 (3) ◽  
pp. 181-192
Author(s):  
Sam Ekstrand ◽  
Peter Wallenberg
Keyword(s):  
Climate Change ◽  
Climate Change Impact ◽  
Algal Blooms ◽  
Climate Model ◽  
Regional Climate ◽  
Nutrient Loss ◽  
Snow Melt ◽  
Market Demand ◽  
Change Impact ◽  
The Impact

Eutrophication manifested by massive algal blooms is the most acute problem in many coastal waters and lakes around the world. The main source of phosphorus and nitrogen transport in the region studied, the Baltic Sea basin, is agriculture. Several studies have predicted adverse climate change impact on eutrophication problems. Here we show that in regions with a climate characterized by pronounced winter seasons and snow-melt flow peaks, this may not be the case. Regional Climate Model results were used to drive hydrological and nutrient modelling. Substantial reductions of phosphorus losses and small reductions for nitrogen were predicted. The main factors behind these results were fewer and less pronounced snow-melt occasions, elevated plant uptake of nutrients and increased growth and crop yield. Based on IPCC scenarios, one ‘market driven’ (A2) and one ‘Local Sustainability’ (B2), the impact on nutrient loss of societal development and future policy lines were assessed. Agricultural adaptation to a future climate, market demand and remedial action policies, e.g. more autumn crops and bio-fuel production, gave further reductions in nutrient loss. The results should not be taken as a motive to reduce efforts to minimize eutrophication in these areas, since severe eutrophication may cause irreversible effects in the decades to come.

scholarly journals Is bias correction of regional climate model (RCM) simulations possible for non-stationary conditions?

2013 ◽  
Vol 17 (12) ◽  
pp. 5061-5077 ◽  
Author(s):  
C. Teutschbein ◽  
J. Seibert
Keyword(s):  
Climate Change ◽  
Bias Correction ◽  
Climate Change Impact ◽  
Climate Model ◽  
Regional Climate ◽  
Model Errors ◽  
Climate Conditions ◽  
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. Due to systematic and random model errors, however, RCM simulations often show considerable deviations from observations. This has led to the development of a number of correction approaches that rely on the assumption that RCM errors do not change over time. It is in principle not possible to test whether this underlying assumption of error stationarity is actually fulfilled for future climate conditions. In this study, however, we demonstrate that it is possible to evaluate how well correction methods perform for conditions different from those used for calibration with the relatively simple differential split-sample test. For five Swedish catchments, precipitation and temperature simulations from 15 different RCMs driven by ERA40 (the 40 yr reanalysis product of the European Centre for Medium-Range Weather Forecasts (ECMWF)) were corrected with different commonly used bias correction methods. We then performed differential split-sample tests by dividing the data series into cold and warm respective dry and wet years. This enabled us to cross-evaluate the performance of different correction procedures under systematically varying climate conditions. The differential split-sample test identified major differences in the ability of the applied correction methods to reduce model errors and to cope with non-stationary biases. More advanced correction methods performed better, whereas large deviations remained for climate model simulations corrected with simpler approaches. Therefore, we question the use of simple correction methods such as the widely used delta-change approach and linear transformation for RCM-based climate-change impact studies. Instead, we recommend using higher-skill correction methods such as distribution mapping.

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