scholarly journals The Role of Forests in Mitigating Climate Change – a Case Study for Europe

2012 ◽  
Vol 8 (1) ◽  
pp. 87-102 ◽  
Author(s):  
Borbála Gálos ◽  
Andreas Hänsler ◽  
Georg Kindermann ◽  
Diana Rechid ◽  
Kevin Sieck ◽  
...  
Keyword(s):  
Climate Change ◽  
Forest Cover ◽  
Climate Model ◽  
Regional Climate ◽  
Regional Scale ◽  
Forest Policy ◽  
Relative Effect ◽  
Climate Change Signal ◽  
Precipitation Decrease

Abstract - A regional-scale case study has been carried out to assess the possible climatic benefits of forest cover increase in Europe. For the end of the 21st century (2071-2090) it has been investigated, whether the projected climate change could be reduced assuming potential afforestation of the continent. The magnitude of the biogeophysical effects of enhanced forest cover on temperature and precipitation means and extremes have been analyzed relative to the magnitude of the climate change signal applying the regional climate model REMO. The simulation results indicate that in the largest part of the temperate zone potential afforestation may reduce the projected climate change through cooler and moister conditions, thus could contribute to the mitigation of the projected climate change for the entire summer period. The largest relative effect of forest cover increase can be expected in northern Germany, Poland and Ukraine. Here, the projected precipitation decrease could be fully compensated, the temperature increase could be relieved by up to 0.5 °C, and the probability of extremely warm and dry days could be reduced. Results can help to identify the areas, where forest cover increase could be the most effective from climatic point of view. Thus they can build an important basis of the future adaptation strategies and forest policy.

On the added value of the regional climate model REMO in the assessment of climate change signal over Central Africa

2017 ◽  
Vol 49 (11-12) ◽  
pp. 3813-3838 ◽  
Author(s):  
Thierry C. Fotso-Nguemo ◽  
Derbetini A. Vondou ◽  
Wilfried M. Pokam ◽  
Zéphirin Yepdo Djomou ◽  
Ismaïla Diallo ◽  
...  
Keyword(s):  
Climate Change ◽  
Regional Climate Model ◽  
Climate Model ◽  
Regional Climate ◽  
Central Africa ◽  
Added Value ◽  
Climate Change Signal

scholarly journals How Does a Regional Climate Model Modify the Projected Climate Change Signal of the Driving GCM: A Study over Different CORDEX Regions Using REMO

Atmosphere ◽  
2013 ◽  
Vol 4 (2) ◽  
pp. 214-236 ◽  
Author(s):  
Claas Teichmann ◽  
Bastian Eggert ◽  
Alberto Elizalde ◽  
Andreas Haensler ◽  
Daniela Jacob ◽  
...  
Keyword(s):  
Climate Change ◽  
Regional Climate Model ◽  
Climate Model ◽  
Regional Climate ◽  
Climate Change Signal

scholarly journals Brief Communication: An update of the article "Modelling flood damages under climate change conditions – a case study for Germany"

2016 ◽  
Vol 16 (7) ◽  
pp. 1617-1622 ◽  
Author(s):  
Fred Fokko Hattermann ◽  
Shaochun Huang ◽  
Olaf Burghoff ◽  
Peter Hoffmann ◽  
Zbigniew W. Kundzewicz
Keyword(s):  
Climate Change ◽  
Large Scale ◽  
Considerable Increase ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Regional Climate ◽  
Impact Modelling ◽  
Flood Damages

Abstract. In our first study on possible flood damages under climate change in Germany, we reported that a considerable increase in flood-related losses can be expected in a future warmer climate. However, the general significance of the study was limited by the fact that outcome of only one global climate model (GCM) was used as a large-scale climate driver, while many studies report that GCMs are often the largest source of uncertainty in impact modelling. Here we show that a much broader set of global and regional climate model combinations as climate drivers show trends which are in line with the original results and even give a stronger increase of damages.

scholarly journals Brief Communication: An update of the article "Modeling flood damages under climate change conditions – a case study for Germany"

2015 ◽  
Vol 3 (12) ◽  
pp. 7231-7245
Author(s):  
F. F. Hattermann ◽  
S. Huang ◽  
O. Burghoff ◽  
P. Hoffmann ◽  
Z. W. Kundzewicz
Keyword(s):  
Climate Change ◽  
Regional Climate Model ◽  
Large Scale ◽  
Considerable Increase ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Regional Climate ◽  
Flood Damages

Abstract. In our first study on possible flood damages under climate change in Germany, we reported that a considerable increase in flood related losses can be expected in future, warmer, climate. However, the general significance of the study was limited by the fact that outcome of only one Global Climate Model (GCM) was used as large scale climate driver, while many studies report that GCM models are often the largest source of uncertainty in impact modeling. Here we show that a much broader set of global and regional climate model combinations as climate driver shows trends which are in line with the original results and even give a stronger increase of damages.

scholarly journals Water supply patterns over Germany under climate change conditions

2013 ◽  
Vol 10 (5) ◽  
pp. 2959-2972 ◽  
Author(s):  
M. H. Tölle ◽  
C. Moseley ◽  
O. Panferov ◽  
G. Busch ◽  
A. Knohl
Keyword(s):  
Climate Change ◽  
Water Supply ◽  
Climate Model ◽  
Regional Climate ◽  
Standardized Precipitation Index ◽  
Heat Fluxes ◽  
Climate Change Signal ◽  
Reference Period ◽  
The Mean ◽  
Time Periods

Abstract. A large ensemble of 24 bias-corrected and uncorrected regional climate model (RCM) simulations is used to investigate climate change impacts on water supply patterns over Germany using the seasonal winter and summer Standardized Precipitation Index (SPI) based on 6-month precipitation sums. The climate change signal is studied comparing SPI characteristics for the reference period 1971–2000 with those of the "near" (2036–2065) and the "far" (2071–2100) future. The spread of the climate change signal within the simulation ensemble of bias-corrected versus non-corrected data is discussed. Ensemble scenarios are evaluated against available observation-based data over the reference period 1971–2000. After correcting the model biases, the model ensemble underestimates the variability of the precipitation climatology in the reference period, but replicates the mean characteristics. Projections of water supply patterns based on the SPI for the time periods 2036–2065 and 2071–2100 show wetter winter months during both future time periods. As a result soil drying may be delayed to late spring extending into the summer period, which could have an important effect on sensible heat fluxes. While projections indicate wetting in summer during 2036–2065, drier summers are estimated towards the south-west of Germany for the end of the 21st century. The use of the bias correction intensifies the signal to wetter conditions for both seasons and time periods. The spread in the projection of future water supply patterns between the ensemble members is explored, resulting in high spatial differences that suggest a higher uncertainty of the climate change signal in the southern part of Germany. It is shown that the spread of the climate change signals between SPIs based on single ensemble members is twice as large as the difference between the mean climate change signal of SPIs based on bias-corrected and uncorrected precipitation. This implies that the sensitivity of the SPI to the modelled precipitation bias is small compared to the range of the climate change signals within our ensemble. Therefore, the SPI is a very useful tool for climate change studies allowing us to avoid the additional uncertainties caused by bias corrections.

scholarly journals Impact of Climate Change on Some Agricultural Crops Distribution and Productivity in Georgia

2021 ◽  
Vol 24 (s1) ◽  
pp. 20-26
Author(s):  
Lia Megrelidze ◽  
Nato Kutaladze ◽  
Gizo Gogichaishvili ◽  
Marina Shvangiradze
Keyword(s):  
Climate Change ◽  
Management Practices ◽  
Climate Model ◽  
Crop Yields ◽  
Regional Climate ◽  
Regional Scale ◽  
Circulation Model ◽  
Global Circulation Model ◽  
Adaptation Measures ◽  
Aquacrop Model

Abstract Under the increase of the concern for food security in the world, mainly caused by water resources shortages, the forecast and determination of crop yield at regional scale has been considered as a strategic topic. This study has been conducted to assess the possible impacts of the climate change on cereal crops productivity and irrigation requirement for two main producing regions of Georgia, according to the current crop pattern, and for the 2050s periods. With this aim, water-driven FAO-AquaCrop model has been used. Furthermore, ongoing and forecasted changes, up to the end of the century, in agro-climatic zones relevant for cereals production have been assessed. The climate change data was generated for RCP4.5 scenario through the global circulation model ECHAM4.1, dynamically downscaled on the region via regional climate model (RegCM4.1). Results show overall increase in cereal crop yields, but also enhancement in water shortages even considering optimum management practices under rainfed conditions. Based on the results obtained, recommendations have been developed for adaptation measures to the climate change for the Georgia Agriculture sector.

scholarly journals The representation of location by regional climate models in complex terrain

2015 ◽  
Vol 12 (3) ◽  
pp. 3011-3028 ◽  
Author(s):  
D. Maraun ◽  
M. Widmann
Keyword(s):  
Climate Change ◽  
Bias Correction ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Climate Change Signal ◽  
Model Errors ◽  
Model Simulations ◽  
Local Grid ◽  
Climate Model Simulations

Abstract. To assess potential impacts of climate change for a specific location, one typically employs climate model simulations at the grid box corresponding to the same geographical location. But based on regional climate model simulations, we show that simulated climate might be systematically displaced compared to observations. In particular in the rain shadow of moutain ranges, a local grid box is therefore often not representative of observed climate: the simulated windward weather does not flow far enough across the mountains; local grid boxes experience the wrong airmasses and atmospheric circulation. In some cases, also the local climate change signal is deteriorated. Classical bias correction methods fail to correct these location errors. Often, however, a distant simulated time series is representative of the considered observed precipitation, such that a non-local bias correction is possible. These findings also clarify limitations of bias correcting global model errors, and of bias correction against station data.

scholarly journals New Insights in Regional Climate Change: Coupled Land Albedo Change Estimation in Greenland from 1981 to 2017

2020 ◽  
Vol 12 (5) ◽  
pp. 756
Author(s):  
Fei Peng ◽  
Haoran Zhou ◽  
Gong Chen ◽  
Qi Li ◽  
Yongxing Wu ◽  
...  
Keyword(s):  
Climate Change ◽  
Land Surface ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Regional Scale ◽  
Spatiotemporal Variation ◽  
Future Climate Change ◽  
Anthropogenic Factors ◽  
The Past

Land albedo is an essential variable in land surface energy balance and climate change. Within regional land, albedo has been altered in Greenland as ice melts and runoff increases in response to global warming against the period of the pre-industrial revolution. The assessment of spatiotemporal variation in albedo is a prerequisite for accurate prediction of ice sheet loss and future climate change, as well as crucial prior knowledge for improving current climate models. In our study, we employed the satellite data product from the global land surface satellite (GLASS) project to obtain the spatiotemporal variation of albedo from 1981 to 2017 using the non-parameter-based M-K (Mann-Kendall) method. It was found that the albedo generally showed a decreasing trend in the past 37 years (−0.013 ± 0.001 decade−1, p < 0.01); in particular, the albedo showed a significant increasing trend in the middle part of the study area but a decreasing trend in the coastal area. The interannual and seasonal variations of albedo showed strong spatial-temporal heterogeneity. Additionally, based on natural and anthropogenic factors, in order to further reveal the potential effects of spatiotemporal variation of albedo on the regional climate, we coupled climate model data with observed data documented by satellite and adopted a conceptual experiment for detections and attributions analysis. Our results showed that both the greenhouse gas forcing and aerosol forcing induced by anthropogenic activities in the past 37 decades were likely to be the main contributors (46.1%) to the decrease of albedo in Greenland. Here, we indicated that overall, Greenland might exhibit a local warming effect based on our study. Albedo–ice melting feedback is strongly associated with local temperature changes in Greenland. Therefore, this study provides a potential pathway to understanding climate change on a regional scale based on the coupled dataset.

scholarly journals Impacts of climate change on extreme floods in Finland – studies using bias corrected Regional Climate Model data

2017 ◽  
Author(s):  
Noora Veijalainen ◽  
Juho Jakkila ◽  
Taru Olsson ◽  
Leif Backman ◽  
Bertel Vehviläinen ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Control Period ◽  
Regional Climate Models ◽  
Heavy Rain ◽  
Climate Change Signal ◽  
Winter Temperatures ◽  
Delta Change

Abstract. Bias correction of precipitation and temperature of five Regional Climate Models (RCMs) was carried out using Distribution Based Scaling (DBS) method with two versions for precipitation adjustment: single gamma and double gamma. This data were then used as input for a hydrological model to simulate changes in floods by the end of this century, and the results were compared to corresponding changes simulated using delta change approach. The results show that while the DBS adjustment significantly improves the RCM precipitations and temperatures compared to observations, especially the double gamma distribution does not always preserve trends of the uncorrected RCM data. The simulation of floods in the control period is improved by the DBS adjustment with no significant differences between single and double gamma. However, some scenarios are still unable to match the observed hydrology adequately due to remaining biases especially in near zero winter temperatures. These scenarios may produce an unrealistic climate change signal and should therefore be discarded from further use. A simple criterion for evaluating the adequate performance of the RCMs and hydrological models compared to observed floods is presented. The results of climate change simulations show that extreme summer precipitations increase more than average values in Finland. The changes in floods by 2070–2099 vary in different regions depending on season and the main flood producing mechanism (snowmelt or heavy rain). The changes in floods simulated with the DBS adjusted RCM data are mostly similar as with delta change approach, but the DBS method produces larger range of changes.

scholarly journals Regional climate change projections from NA-CORDEX and their relation to climate sensitivity

2020 ◽  
Vol 162 (2) ◽  
pp. 645-665
Author(s):  
Melissa S. Bukovsky ◽  
Linda O. Mearns
Keyword(s):  
Climate Change ◽  
Climate Sensitivity ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Regional Climate ◽  
Full Range ◽  
Regional Scale ◽  
Global Climate Models ◽  
Continental Scale

Abstract The climate sensitivity of global climate models (GCMs) strongly influences projected climate change due to increased atmospheric carbon dioxide. Reasonably, the climate sensitivity of a GCM may be expected to affect dynamically downscaled projections. However, there has been little examination of the effect of the climate sensitivity of GCMs on regional climate model (RCM) ensembles. Therefore, we present projections of temperature and precipitation from the ensemble of projections produced as a part of the North American branch of the international Coordinated Regional Downscaling Experiment (NA-CORDEX) in the context of their relationship to the climate sensitivity of their parent GCMs. NA-CORDEX simulations were produced at 50-km and 25-km resolutions with multiple RCMs which downscaled multiple GCMs that spanned nearly the full range of climate sensitivity available in the CMIP5 archive. We show that climate sensitivity is a very important source of spread in the NA-CORDEX ensemble, particularly for temperature. Temperature projections correlate with driving GCM climate sensitivity annually and seasonally across North America not only at a continental scale but also at a local-to-regional scale. Importantly, the spread in temperature projections would be reduced if only low, mid, or high climate sensitivity simulations were considered, or if only the ensemble mean were considered. Precipitation projections correlate with climate sensitivity, but only at a continental scale during the cold season, due to the increasing influence of other processes at finer scales. Additionally, it is shown that the RCMs do alter the projection space sampled by their driving GCMs.

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