Potential impacts of climate change on European wind energy resource under the CMIP5 future climate projections

<p>Coarse resolution global climate models (GCM) cannot resolve fine-scale drivers of regional climate, which is the scale where climate adaptation decisions are made. Regional climate models (RCMs) generate high-resolution projections by dynamically downscaling GCM outputs. However, evidence of where and when downscaling provides new information about both the current climate (added value, AV) and projected climate change signals, relative to driving data, is lacking. Seasons and locations where CORDEX-Australasia ERA-Interim and GCM-driven RCMs show AV for mean and extreme precipitation and temperature are identified. A new concept is introduced, &#8216;realised added value&#8217;, that identifies where and when RCMs simultaneously add value in the present climate and project a different climate change signal, thus suggesting plausible improvements in future climate projections by RCMs. ERA-Interim-driven RCMs add value to the simulation of summer-time mean precipitation, especially over northern and eastern Australia. GCM-driven RCMs show AV for precipitation over complex orography in south-eastern Australia during winter and widespread AV for mean and extreme minimum temperature during both seasons, especially over coastal and high-altitude areas. RCM projections of decreased winter rainfall over the Australian Alps and decreased summer rainfall over northern Australia are collocated with notable realised added value. Realised added value averaged across models, variables, seasons and statistics is evident across the majority of Australia and shows where plausible improvements in future climate projections are conferred by RCMs. This assessment of varying RCM capabilities to provide realised added value to GCM projections can be applied globally to inform climate adaptation and model development.</p>

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A comparison between direct and indirect frameworks to evaluate impacts of climate change on streamflows: case study of Karkheh River basin in Iran

Journal of Water and Climate Change ◽

10.2166/wcc.2017.043 ◽

2017 ◽

Vol 8 (4) ◽

pp. 652-674 ◽

Cited By ~ 6

Author(s):

Mohsen Nasseri ◽

Banafsheh Zahraie ◽

Leila Forouhar

Keyword(s):

Climate Change ◽

River Basin ◽

Hydrological Modeling ◽

Future Climate ◽

Multivariate Adaptive Regression Splines ◽

Group Method ◽

Climate Projections ◽

Climate Conditions ◽

Karkheh River Basin ◽

Impacts Of Climate Change

Abstract In this paper, two approaches to assess the impacts of climate change on streamflows have been used. In the first approach (direct), a statistical downscaling technique was utilized to predict future streamflows based on large-scale data of general circulation models (GCMs). In the second approach (indirect), GCM outputs were downscaled to produce local climate conditions which were then used as inputs to a hydrological simulation model. In this article, some data-mining methods such as model-tree, multivariate adaptive regression splines and group method of data handling were utilized for direct downscaling of streamflows. Projections of HadCM3 model for A2 and B2 SRES scenarios were also used to simulate future climate conditions. These evaluations were done over three sub-basins of Karkheh River basin in southwest Iran. To achieve a comprehensive assessment, a global uncertainty assessment method was used to evaluate the results of the models. The results indicated that despite simplifications included in the direct downscaling, this approach is accurate enough to be used for assessing climate change impacts on streamflows without computational efforts of hydrological modeling. Furthermore, comparing future climate projections, the uncertainty associated with elimination of hydrological modeling is estimated to be high.

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Distributed modelling of climate change impacts on snow sublimation in Northern Mongolia

Advances in Geosciences ◽

10.5194/adgeo-21-117-2009 ◽

2009 ◽

Vol 21 ◽

pp. 117-124 ◽

Cited By ~ 19

Author(s):

F. Wimmer ◽

S. Schlaffer ◽

T. aus der Beek ◽

L. Menzel

Keyword(s):

Climate Change ◽

Hydrological Cycle ◽

Interpolation Method ◽

Meteorological Parameter ◽

Ordinary Least Squares ◽

Future Climate ◽

Data Availability ◽

Climate Projections ◽

Global Circulation Models ◽

Future Climate Projections

Abstract. Sublimation of snow is an important factor of the hydrological cycle in Mongolia and is likely to increase according to future climate projections. In this study the hydrological model TRAIN was used to assess spatially distributed current and future sublimation rates based on interpolated daily data of precipitation, air temperature, air humidity, wind speed and solar radiation. An automated procedure for the interpolation of the input data is provided. Depending on the meteorological parameter and the data availability for the individual days, the most appropriate interpolation method is chosen automatically from inverse distance weighting, Ordinary Least Squares interpolation, Ordinary or Universal Kriging. Depending on elevation simulated annual sublimation in the period 1986–2006 was 23 to 35 mm, i.e. approximately 80% of total snowfall. Moreover, future climate projections for 2071–2100 of ECHAM5 and HadCM3, based on the A1B emission scenario of the Intergovernmental Panel on Climate Change, were analysed with TRAIN. In the case of ECHAM5 simulated sublimation increases by up to 17% (26...41 mm) while it remains at the same level for HadCM3 (24...34 mm). The differences are mainly due to a distinct increase in winter precipitation for ECHAM5. Simulated changes of the all-season hydrological conditions, e.g. the sublimation-to-precipitation ratio, were ambiguous due to diverse precipitation patterns derived by the global circulation models.

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Climate change signal of future climate projections for Aachen, Germany, in terms of temperature and precipitation

Meteorologische Zeitschrift ◽

10.1127/0941-2948/2014/0549 ◽

2014 ◽

Vol 23 (1) ◽

pp. 63-74 ◽

Cited By ~ 2

Author(s):

Mareike Buttstädt ◽

Christoph Schneider

Keyword(s):

Climate Change ◽

Future Climate ◽

Climate Change Signal ◽

Climate Projections ◽

Temperature And Precipitation ◽

Future Climate Projections

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Reducing Uncertainties in Future Climate Projections by Quantifying Human COntributions to 20th Century Climate Change

10.2172/1052208 ◽

2011 ◽

Author(s):

Gabriele Hegerl and IDAG members

Keyword(s):

Climate Change ◽

20Th Century ◽

Future Climate ◽

Climate Projections ◽

Future Climate Projections

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What are we Doing about Climate Change?

Human Geography ◽

10.1177/194277861000300201 ◽

2010 ◽

Vol 3 (2) ◽

pp. 1-20

Author(s):

Helen M. Cox

Keyword(s):

Climate Change ◽

Global Warming ◽

Environmental Problem ◽

Climate Science ◽

Future Climate ◽

Climate Projections ◽

The Press ◽

The World ◽

The Subject ◽

Future Climate Projections

Climate change is the most important contemporary environmental problem that the world faces, yet it is the subject of many misconceptions. Climate science has been used for political ends and distorted in the press, both intentionally and through ignorance. This article presents an overview of what is known about global warming and what is controversial, about future climate projections and their impacts, and about the emissions responsible for climate change and policies to limit them.

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Impacts of Climate Change on the Potential Productivity of Eleven Staple Crops in Rwanda

Sustainability ◽

10.3390/su12104116 ◽

2020 ◽

Vol 12 (10) ◽

pp. 4116

Author(s):

Kemen Austin ◽

Robert Beach ◽

Daniel Lapidus ◽

Marwa Salem ◽

Naomi Taylor ◽

...

Keyword(s):

Climate Change ◽

Agricultural Sector ◽

Crop Yields ◽

Future Climate ◽

Environmental Data ◽

Future Climate Change ◽

Climate Projections ◽

Estimation Model ◽

Potential Productivity ◽

Impacts Of Climate Change

This study quantifies the potential responses of 11 staple crop yields to projected changes in temperature and precipitation in Rwanda, using a cross sectional model based on yield data collected across more than 14,000 villages. We incorporated a relatively high spatial resolution dataset on crop productivity, considered a broad range of crops relevant to national agricultural production priorities, used environmental data developed specifically for Rwanda, and reported uncertainty both from our estimation model and due to uncertainty in future climate projections. We estimate that future climate change will have the largest impacts on potential productivity of maize, bush bean, and Irish potato. All three crops are likely to experience a reduction in potential yields of at least 10% under Representative Concentration Pathway (RCP) 4.5 and at least 15% under RCP 8.5 by 2050. Notably, these are important crops nationally, and three of the crops targeted by Rwanda’s Crop Intensification Program. We find that the most severe reductions in potential crop yields will occur in the drier eastern savannah and plateau regions, but that the impacts of climate change could be neutral or even positive in the highlands through mid-century. The refined spatial scale of our analysis allows us to identify potentially vulnerable regions where adaptation investments may need to be prioritized to support food security and climate resilience in Rwanda’s agricultural sector.

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The Impact of Global Warming on Wind Energy Resources and Ramp Events in Japan

Atmosphere ◽

10.3390/atmos10050265 ◽

2019 ◽

Vol 10 (5) ◽

pp. 265 ◽

Cited By ~ 6

Author(s):

Masamichi Ohba

Keyword(s):

Global Warming ◽

Wind Energy ◽

Future Climate ◽

Energy Resources ◽

Climate Projections ◽

Advantages And Disadvantages ◽

Ramp Events ◽

Wind Energy Resources ◽

The Impact ◽

Future Climate Projections

This study investigated the impact of global warming on Japanese wind energy resources and their short-term variations using the large ensemble d4PDF dataset, which consists of dynamically downscaled historical and +4K future climate projections. The capacity factor under the future and present climate was estimated from an idealized power curve based on hourly near-surface wind speeds. The +4K warming future climate projections showed significant changes in wind energy resources that varied both regionally and seasonally. The wind energy potential was projected to slightly increase (decrease) from winter to spring over northern (southern) Japan and decrease from summer to autumn over most of Japan. The projected annual production decreased by about ~5% over Japan in response to climate change. The frequency of wind ramp events also decreased in the latter seasons. The relationship to synoptic weather was investigated using self-organizing maps, whereby weather patterns (WPs) over the region in the present and future +4K climate were classified for a two-dimensional lattice. Future probabilistic projections of WPs under the global warming scenario showed both increases and decreases in the frequency of different WPs, with corresponding advantages and disadvantages for wind power generation with regard to future changes in capacity factors in Japan. The importance of these frequency changes on the total change was further assessed by separating the dynamical and thermodynamic contributions.

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Climate change projections of maximum temperature in the pre-monsoon season in Bangladesh using statistical downscaling of global climate models

Advances in Science and Research ◽

10.5194/asr-18-99-2021 ◽

2021 ◽

Vol 18 ◽

pp. 99-114

Author(s):

M. Bazlur Rashid ◽

Syed Shahadat Hossain ◽

M. Abdul Mannan ◽

Kajsa M. Parding ◽

Hans Olav Hygen ◽

...

Keyword(s):

Climate Change ◽

Statistical Downscaling ◽

Climate Models ◽

Global Climate ◽

Monsoon Season ◽

Global Climate Models ◽

Future Climate ◽

Maximum Temperature ◽

Climate Projections ◽

Future Climate Projections

Abstract. The climate of Bangladesh is very likely to be influenced by global climate change. To quantify the influence on the climate of Bangladesh, Global Climate Models were downscaled statistically to produce future climate projections of maximum temperature during the pre-monsoon season (March–May) for the 21st century for Bangladesh. The future climate projections are generated based on three emission scenarios (RCP2.6, RCP4.5 and RCP8.5) provided by the fifth Coupled Model Intercomparison Project. The downscaling process is undertaken by relating the large-scale seasonal mean temperature, taken from the ERA5 reanalysis data set, to the leading principal components of the observed maximum temperature at stations under Bangladesh Meteorological Department in Bangladesh, and applying the relationship to the GCM ensemble. The in-situ temperature data has only recently been digitised, and this is the first time they have been used in statistical downscaling of local climate projections for Bangladesh. This analysis also provides an evaluation of the local data, and the local temperatures in Bangladesh show a close match with the ERA5 reanalysis. Compared to the reference period of 1981–2010, the projected maximum pre-monsoon temperature in Bangladesh indicate an increase by 0.7/0.7/0.7 ∘C in the near future (2021–2050) and 2.2/1.2/0.8 ∘C in the far future (2071–2100) assuming the RCP8.5/RCP4.5/RCP2.6 scenario, respectively.

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