scholarly journals Quantifying the Likelihood of Regional Climate Change: A Hybridized Approach

2013 ◽  
Vol 26 (10) ◽  
pp. 3394-3414 ◽  
Author(s):  
C. Adam Schlosser ◽  
Xiang Gao ◽  
Kenneth Strzepek ◽  
Andrei Sokolov ◽  
Chris E. Forest ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Climate Projections ◽  
Massachusetts Institute Of Technology ◽  
Adaptation To Climate Change ◽  
Intergovernmental Panel ◽  
Near Surface ◽  
Institute Of Technology

Abstract The growing need for risk-based assessments of impacts and adaptation to climate change calls for increased capability in climate projections: specifically, the quantification of the likelihood of regional outcomes and the representation of their uncertainty. Herein, the authors present a technique that extends the latitudinal projections of the 2D atmospheric model of the Massachusetts Institute of Technology (MIT) Integrated Global System Model (IGSM) by applying longitudinally resolved patterns from observations, and from climate model projections archived from exercises carried out for the Fourth Assessment Report (AR4) of the Intergovernmental Panel on Climate Change (IPCC). The method maps the IGSM zonal means across longitude using a set of transformation coefficients, and this approach is demonstrated in application to near-surface air temperature and precipitation, for which high-quality observational datasets and model simulations of climate change are available. The current climatology of the transformation coefficients is observationally based. To estimate how these coefficients may alter with climate, the authors characterize the climate models’ spatial responses, relative to their zonal mean, from transient increases in trace-gas concentrations and then normalize these responses against their corresponding transient global temperature responses. This procedure allows for the construction of metaensembles of regional climate outcomes, combining the ensembles of the MIT IGSM—which produce global and latitudinal climate projections, with uncertainty, under different global climate policy scenarios—with regionally resolved patterns from the archived IPCC climate model projections. This hybridization of the climate model longitudinal projections with the global and latitudinal patterns projected by the IGSM can, in principle, be applied to any given state or flux variable that has the sufficient observational and model-based information.

scholarly journals Regional Climate Model Emulator based on Deep Learning: Concept and First Evaluation of a Novel Hybrid Downscaling Approach

2021 ◽  
Author(s):  
Antoine Doury ◽  
Samuel Somot ◽  
Sébastien Gadat ◽  
Aurélien Ribes ◽  
Lola Corre
Keyword(s):  
Climate Change ◽  
Network Architecture ◽  
Large Scale ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Future Climate ◽  
Near Surface ◽  
The Way

Abstract Providing reliable information on climate change at local scale remains a challenge of first importance for impact studies and policymakers. Here, we propose a novel hybrid downscaling method combining the strengths of both empirical statistical downscaling methods and Regional Climate Models (RCMs). The aim of this tool is to enlarge the size of high-resolution RCM simulation ensembles at low cost.We build a statistical RCM-emulator by estimating the downscaling function included in the RCM. This framework allows us to learn the relationship between large-scale predictors and a local surface variable of interest over the RCM domain in present and future climate. Furthermore, the emulator relies on a neural network architecture, which grants computational efficiency. The RCM-emulator developed in this study is trained to produce daily maps of the near-surface temperature at the RCM resolution (12km). The emulator demonstrates an excellent ability to reproduce the complex spatial structure and daily variability simulated by the RCM and in particular the way the RCM refines locally the low-resolution climate patterns. Training in future climate appears to be a key feature of our emulator. Moreover, there is a huge computational benefit in running the emulator rather than the RCM, since training the emulator takes about 2 hours on GPU, and the prediction is nearly instantaneous. However, further work is needed to improve the way the RCM-emulator reproduces some of the temperature extremes, the intensity of climate change, and to extend the proposed methodology to different regions, GCMs, RCMs, and variables of interest.

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>

Comparison of different infiltration schemes in the regional climate model REMO

2021 ◽  
Author(s):  
Daniel Abel ◽  
Katrin Ziegler ◽  
Felix Pollinger ◽  
Heiko Paeth
Keyword(s):  
Regional Climate Model ◽  
Climate Models ◽  
Climate Model ◽  
Root Zone ◽  
Regional Climate ◽  
Climate Projections ◽  
Soil Hydrology ◽  
Spatiotemporal Resolution ◽  
Development Fund ◽  
Near Surface

<p>The European Regional Development Fund-Project BigData@Geo aims to create highly resolved climate projections for the model region of Lower Franconia in Bavaria, Germany. These projections are analyzed and made available to local stakeholders of agriculture, forestry, and viniculture as well as general public. Since regional climate models’ spatiotemporal resolution often is too coarse to deal with such local issues, the regional climate model REMO is improved within the frame of the project in cooperation with the Climate Service Center Germany (GERICS).</p><p>Accurate and highly resolved climate projections require realistic modeling of soil hydrology. Thus, REMO’s original bucket scheme is replaced by a 5-layer soil scheme. It allows for the representation of water below the root zone. Evaporation is possible solely from the top layer instead of the entire bucket and water can flow vertically between the layers. Consequently, the properties and processes change significantly compared to the bucket scheme. Both, the bucket and the 5-layer scheme, use the improved Arno scheme to separate throughfall into infiltration and surface runoff.</p><p>In this study, we examine if this scheme is suitable for use with the improved soil hydrology or if other schemes lead to better results. For this, we (1) modify the improved Arno scheme and further introduce the infiltration equations of (2) Philip as well as (3) Green and Ampt. First results of the comparison of these four different schemes and their influence on soil moisture and near-surface atmospheric variables are presented.</p>

scholarly journals Do Convection-Permitting Regional Climate Models Improve Projections of Future Precipitation Change?

2017 ◽  
Vol 98 (1) ◽  
pp. 79-93 ◽  
Author(s):  
Elizabeth J. Kendon ◽  
Nikolina Ban ◽  
Nigel M. Roberts ◽  
Hayley J. Fowler ◽  
Malcolm J. Roberts ◽  
...  
Keyword(s):  
Climate Change ◽  
Large Scale ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate Model ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Climate Projections ◽  
Convective Storms ◽  
Wide Range

Abstract Regional climate projections are used in a wide range of impact studies, from assessing future flood risk to climate change impacts on food and energy production. These model projections are typically at 12–50-km resolution, providing valuable regional detail but with inherent limitations, in part because of the need to parameterize convection. The first climate change experiments at convection-permitting resolution (kilometer-scale grid spacing) are now available for the United Kingdom; the Alps; Germany; Sydney, Australia; and the western United States. These models give a more realistic representation of convection and are better able to simulate hourly precipitation characteristics that are poorly represented in coarser-resolution climate models. Here we examine these new experiments to determine whether future midlatitude precipitation projections are robust from coarse to higher resolutions, with implications also for the tropics. We find that the explicit representation of the convective storms themselves, only possible in convection-permitting models, is necessary for capturing changes in the intensity and duration of summertime rain on daily and shorter time scales. Other aspects of rainfall change, including changes in seasonal mean precipitation and event occurrence, appear robust across resolutions, and therefore coarse-resolution regional climate models are likely to provide reliable future projections, provided that large-scale changes from the global climate model are reliable. The improved representation of convective storms also has implications for projections of wind, hail, fog, and lightning. We identify a number of impact areas, especially flooding, but also transport and wind energy, for which very high-resolution models may be needed for reliable future assessments.

scholarly journals Assessment of the impacts of climate change on maize production in the Wami Ruvu basin of Tanzania

2016 ◽  
Vol 8 (1) ◽  
pp. 142-164 ◽  
Author(s):  
Philbert Luhunga ◽  
Ladslaus Chang'a ◽  
George Djolov
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Regional Climate ◽  
Climate Projections ◽  
Intergovernmental Panel ◽  
Lower Altitude ◽  
Rcp 8.5 ◽  
Maize Yields ◽  
The Impact ◽  
Impacts Of Climate Change

The IPCC (Intergovernmental Panel on Climate Change) assessment reports confirm that climate change will hit developing countries the hardest. Adaption is on the agenda of many countries around the world. However, before devising adaption strategies, it is crucial to assess and understand the impacts of climate change at regional and local scales. In this study, the impact of climate change on rain-fed maize (Zea mays) production in the Wami-Ruvu basin of Tanzania was evaluated using the Decision Support System for Agro-technological Transfer. The model was fed with daily minimum and maximum temperatures, rainfall and solar radiation for current climate conditions (1971–2000) as well as future climate projections (2010–2099) for two Representative Concentration Pathways: RCP 4.5 and RCP 8.5. These data were derived from three high-resolution regional climate models, used in the Coordinated Regional Climate Downscaling Experiment program. Results showed that due to climate change future maize yields over the Wami-Ruvu basin will slightly increase relative to the baseline during the current century under RCP 4.5 and RCP 8.5. However, maize yields will decline in the mid and end centuries. The spatial distribution showed that high decline in maize yields are projected over lower altitude regions due to projected increase in temperatures in those areas.

scholarly journals Climate Change Impact Assessment for Aji Basin Using Statistical Downscaling and Bias Correction of Climate Model Outputs

2016 ◽  
Vol 11 (2) ◽  
pp. 670-678 ◽  
Author(s):  
N. S Vithlani ◽  
H. D Rank
Keyword(s):  
Climate Change ◽  
Bias Correction ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Regional Climate ◽  
Climate Extremes ◽  
Global Climate Models ◽  
Climate Indices ◽  
Climate Projections

For the future projections Global climate models (GCMs) enable development of climate projections and relate greenhouse gas forcing to future potential climate states. When focusing it on smaller scales it exhibit some limitations to overcome this problem, regional climate models (RCMs) and other downscaling methods have been developed. To ensure statistics of the downscaled output matched the corresponding statistics of the observed data, bias correction was used. Quantify future changes of climate extremes were analyzed, based on these downscaled data from two RCMs grid points. Subset of indices and models, results of bias corrected model output and raw for the present day climate were compared with observation, which demonstrated that bias correction is important for RCM outputs. Bias correction directed agreements of extreme climate indices for future climate it does not correct for lag inverse autocorrelation and fraction of wet and dry days. But, it was observed that adjusting both the biases in the mean and variability, relatively simple non-linear correction, leads to better reproduction of observed extreme daily and multi-daily precipitation amounts. Due to climate change temperature and precipitation will increased day by day.

scholarly journals Balanced estimate and uncertainty assessment of European climate change using the large EURO-CORDEX regional climate model ensemble

2021 ◽  
Author(s):  
Guillaume Evin ◽  
Samuel Somot ◽  
Benoit Hingray
Keyword(s):  
Regional Climate Model ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Regional Climate ◽  
Uncertainty Assessment ◽  
Global Climate Models ◽  
Climate Projections ◽  
Total Uncertainty ◽  
Near Surface

Abstract. Large Multiscenarios Multimodel Ensembles (MMEs) of regional climate model (RCM) experiments driven by Global Climate Models (GCM) are made available worldwide and aim at providing robust estimates of climate changes and associated uncertainties. Due to many missing combinations of emission scenarios and climate models leading to sparse Scenario-GCM-RCM matrices, these large ensembles are however very unbalanced, which makes uncertainty analyses impossible with standard approaches. In this paper, the uncertainty assessment is carried out by applying an advanced statistical approach, called QUALYPSO, to a very large ensemble of 87 EURO-CORDEX climate projections, the largest ensemble ever produced for regional projections in Europe. This analysis provides i) the most up-to-date and balanced estimates of mean changes for near-surface temperature and precipitation in Europe, ii) the total uncertainty of projections and its partition as a function of time, and iii) the list of the most important contributors to the model uncertainty. For changes of total precipitation and mean temperature in winter (DJF) and summer (JJA), the uncertainty due to RCMs can be as large as the uncertainty due to GCMs at the end of the century (2071–2099). Both uncertainty sources are mainly due to a small number of individual models clearly identified. Due to the highly unbalanced character of the MME, mean estimated changes can drastically differ from standard average estimates based on the raw ensemble of opportunity. For the RCP4.5 emission scenario in Central-Eastern Europe for instance, the difference between balanced and direct estimates are up to 0.8 °C for summer temperature changes and up to 20 % for summer precipitation changes at the end of the century.

scholarly journals Análisis de anomalías climáticas para la cuenca del río La Villa, Panamá, basado en los escenarios RCP

2020 ◽  
Vol 16 (1) ◽  
pp. 83-89
Author(s):  
Cassilda Saavedra
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Small Scale ◽  
Climate Projections ◽  
Adaptation To Climate Change ◽  
Climate Change Projections ◽  
Intergovernmental Panel ◽  
Rcp Scenarios ◽  
Mitigation And Adaptation ◽  
Del Rio

The use of climate change projections is crucial for mitigation and adaptation, which are the basis for creating resilience. However, access to these scientific products is scarce in Latin America and the existing studies lack of an appropriate resolution to analyze small but highly vulnerable regions, such as river basins for planning purposes.   La Villa river basin, Republic of Panama, is one of the watersheds of highest priority for adaptation to climate change. This study used downscaled projections from four climate models. The models are based on the Representative Concentration Pathways (RCP), presented in the Fifth Assessment Report of the Intergovernmental Panel on Climate Change-IPCC. Results of this study suggest increases of the annual average precipitation in the watershed for the years 2050 and 2070. Meanwhile, maximum and minimum temperatures will increase an average of 1-2 ° C and near 4 ° C by the end of the 21st century. With these results, we observed that the use of small-scale climate projections in the RCP scenarios is feasible to determine the effects of climate change on small regions.

Distribution-based scaling to improve usability of regional climate model projections for hydrological climate change impacts studies

2010 ◽  
Vol 41 (3-4) ◽  
pp. 211-229 ◽  
Author(s):  
Wei Yang ◽  
Johan Andréasson ◽  
L. Phil Graham ◽  
Jonas Olsson ◽  
Jörgen Rosberg ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Climate Change Impacts ◽  
Regional Climate Models ◽  
Climate Projections ◽  
Impact Studies ◽  
Emissions Scenarios ◽  
Precipitation And Temperature

As climate change could have considerable influence on hydrology and corresponding water management, appropriate climate change inputs should be used for assessing future impacts. Although the performance of regional climate models (RCMs) has improved over time, systematic model biases still constrain the direct use of RCM output for hydrological impact studies. To address this, a distribution-based scaling (DBS) approach was developed that adjusts precipitation and temperature from RCMs to better reflect observations. Statistical properties, such as daily mean, standard deviation, distribution and frequency of precipitation days, were much improved for control periods compared to direct RCM output. DBS-adjusted precipitation and temperature from two IPCC Special Report on Emissions Scenarios (SRESA1B) transient climate projections were used as inputs to the HBV hydrological model for several river basins in Sweden for the period 1961–2100. Hydrological results using DBS were compared to results with the widely-used delta change (DC) approach for impact studies. The general signal of a warmer and wetter climate was obtained using both approaches, but use of DBS identified differences between the two projections that were not seen with DC. The DBS approach is thought to better preserve the future variability produced by the RCM, improving usability for climate change impact studies.

Investigation of future climate characteristics over the Carpathian Region by “tuning” CORDEX temperature time series – a new perspective

2021 ◽  
Author(s):  
Csaba Zsolt Torma
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Climate Model ◽  
Standard Procedure ◽  
Regional Climate ◽  
Global Climate Models ◽  
Climate Projections ◽  
Global Changes ◽  
Regional Warming ◽  
Mean Temperature

<p>The answers to the following questions ‘What are the consequences of climate change (warming)…?’ and ‘By when do we have to be prepared for that level of climate change (warming)?’ must be given only with caution. On the one hand, regional or local changes can be inconsistent with global changes, as local processes might not accurately interpreted by global climate models (GCMs) due to their relative coarse resolution. On the other hand, climate model simulations’ outputs are prone to biases compared to observations; furthermore, climate projections can be very different in modelling future temperature characteristics. In this context, while the magnitude of expected change described by a climate model may seem to be reasonable, but the projected temperature is not necessarily realistic (considering the model’s relative bias compared to observations). More specifically, the standard procedure of assessing climate change can be illustrated by taking the mean for a future period (e.g. 2070–2099) and compute the change relative to a reference period (e.g. 1976−2005). Keeping in mind the expected changes based on those projections might come with high degree of uncertainty as simulations might show different mean temperature values for the same assessed periods with even a range of few degrees of °C. When regional climate change is assessed based on at a given regional warming level (WL, e.g. 1.5 °C) added to the observed mean, then the aforementioned uncertainty range is reduced as the models (GCM or regional climate models) are assessed with respect to the same 30-year mean temperature value, but not for the same periods (noting that the WL is defined at regional and not at global scale). Thus the uncertainty of expected changes with regard to temperature can be significantly reduced. In this case an additional uncertainty factor might rise: time, as climate models can reach that WL at different times. Accordingly, we can give information on relative changes with a specific uncertainty as a metric based on the timing of reaching the assessed WL. Aim of the present work is to illustrate the feasibility of this concept for the region of the Carpathian Basin based on high-resolution EURO- and Med-CORDEX simulations.</p>

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