scholarly journals Regional climate change projections for the Barents region

2016 ◽  
Author(s):  
Andreas Dobler ◽  
Jan Erik Haugen ◽  
Rasmus Emil Benestad
Keyword(s):  
Sea Ice ◽  
Regional Climate Model ◽  
Cloud Cover ◽  
Climate Models ◽  
Climate Model ◽  
Barents Sea ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Barents Region ◽  
The Barents Sea

Abstract. Regional climate models can provide estimates for quantities that are difficult to study in empirical studies, such as cloud cover, wind, sea-ice or dependencies between variables. In this study, the regional climate model COSMO-CLM was used to simulate local climate conditions over the Barents region and provide projections for the three emission scenarios RCP2.6, RCP4.5 and RCP8.5. The results indicate that the most pronounced local warming can be expected in winter in the high Arctic near the present sea-ice border. The changes reach up to 20K, resulting in future temperatures close to melting. Similar spatial patterns are seen for changes in precipitation and wind in all scenarios, but with different amplitudes. Precipitation sensitivities, however, show the highest values along the west coast of Norway and in the Arctic during summer. For clouds, the projections show a decrease in winter mean cloud cover over sea and an increase over land, dominated by changes in low layer clouds. Over the Barents sea, convective cloud fraction is projected to increase, together with an increases in convective and total precipitation. In contrast to the COSMO-CLM and two other regional climate models taken into account, the ensemble mean of the driving global models shows an increasing trend in total cloud cover over the Barents sea. An analysis of the opposing trends reveals that there is an added value in the regional climate model projections for the Barents region.

scholarly journals The influence of ocean heat transport in the Barents Sea on the regional sea ice and the atmospheric static stability

2019 ◽  
Vol 59 (4) ◽  
pp. 529-538
Author(s):  
M. G. Akperov ◽  
V. A. Semenov ◽  
I. I. Mokhov ◽  
M. A. Dembitskaya ◽  
D. D. Bokuchava ◽  
...  
Keyword(s):  
Sea Ice ◽  
Regional Climate Model ◽  
Climate Model ◽  
Barents Sea ◽  
Regional Climate ◽  
Static Stability ◽  
The Arctic ◽  
Oceanic Water ◽  
Ensemble Simulations ◽  
The Barents Sea

The influence of the oceanic heat inflow into the Barents Sea on the sea ice concentration and atmospheric characteristics, including the atmospheric static stability during winter months, is investigated on the basis of the results of ensemble simulations with the regional climate model HIRHAM/NAOSIM for the Arctic. The static stability of the atmosphere is the important indicator of the spatial and temporal variability of polar mesocyclones in the Arctic region. The results of the HIRHAM/NAOSIM regional climate model ensemble simulations (RCM) for the period from 1979 to 2016 were used for the analysis. The initial and lateral boundary conditions for RCM in the atmosphere were set in accordance with the ERA-Interim reanalysis data. An analysis of 10 ensemble simulations with identical boundary conditions and the same radiation forcing for the Arctic was performed. Various realizations of ensemble simulations with RCM were obtained by changing the initial conditions for integrating the oceanic block of the model. Different realizations of ensemble simulations with RCM are obtained by changing the initial conditions of the model oceanic block integration. The composites method was used for the analysis, i.e. the difference between the mean values for years with the maximum and minimum inflow of oceanic water into the Barents Sea. The statistical significance of the results (at a significance level of p < 0.05) was estimated using Student's t-test. In general, the regional climate model reproduces the seasonal changes in the inflow of the oceanic water and heat into the Barents Sea reasonably well. There is a strong relationship between the changes in the oceanic water and ocean heat inflow, sea ice concentration, and surface air temperature in the Barents Sea. Herewith, the increase in the oceanic water inflow into the Barents Sea in winter leads to a decrease in static stability, which contributes to changes in regional cyclonic activity. The decrease of the static stability is most pronounced in the southern part of the Barents Sea and also to the west of Svalbard.

Regional Climate Model Inter-Comparison for Antarctica within a Data Science Framework

2021 ◽  
Author(s):  
Jeremy Carter ◽  
Amber Leeson ◽  
Andrew Orr ◽  
Christoph Kittel ◽  
Melchior van Wessem
Keyword(s):  
Regional Climate Model ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Future Climate Change ◽  
Near Surface ◽  
Virtual Lab ◽  
Model Variability ◽  
The Antarctic

&lt;p&gt;Understanding the surface climatology of the Antarctic ice sheet is essential if we are to adequately predict its response to future climate change. This includes both primary impacts such as increased ice melting and secondary impacts such as ice shelf collapse events. Given its size, and inhospitable environment, weather stations on Antarctica are sparse. Thus, we rely on regional climate models to 1) develop our understanding of how the climate of Antarctica varies in both time and space and 2) provide data to use as context for remote sensing studies and forcing for dynamical process models. Given that there are a number of different regional climate models available that explicitly simulate Antarctic climate, understanding inter- and intra model variability is important.&lt;/p&gt;&lt;p&gt;Here, inter- and intra-model variability in Antarctic-wide regional climate model output is assessed for: snowfall; rainfall; snowmelt and near-surface air temperature within a cloud-based virtual lab framework. State-of-the-art regional climate model runs from the Antarctic-CORDEX project using the RACMO, MAR and MetUM models are used, together with the ERA5 and ERA-Interim reanalyses products. Multiple simulations using the same model and domain boundary but run at either different spatial resolutions or with different driving data are used. Traditional analysis techniques are exploited and the question of potential added value from more modern and involved methods such as the use of Gaussian Processes is investigated. The advantages of using a virtual lab in a cloud based environment for increasing transparency and reproducibility, are demonstrated, with a view to ultimately make the code and methods used widely available for other research groups.&lt;/p&gt;

scholarly journals Global Radiative Flux and Cloudiness Variability for the Period 1959–2010 in Belgium: A Comparison between Reanalyses and the Regional Climate Model MAR

Atmosphere ◽  
2018 ◽  
Vol 9 (7) ◽  
pp. 262 ◽  
Author(s):  
Coraline Wyard ◽  
Sébastien Doutreloup ◽  
Alexandre Belleflamme ◽  
Martin Wild ◽  
Xavier Fettweis
Keyword(s):  
Cloud Cover ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Horizontal Resolution ◽  
Radiative Flux ◽  
Added Value ◽  
Historical Trends ◽  
The Mean

The use of regional climate models (RCMs) can partly reduce the biases in global radiative flux (Eg↓) that are found in reanalysis products and global models, as they allow for a finer spatial resolution and a finer parametrisation of surface and atmospheric processes. In this study, we assess the ability of the MAR («Modèle Atmosphérique Régional») RCM to reproduce observed changes in Eg↓, and we investigate the added value of MAR with respect to reanalyses. Simulations were performed at a horizontal resolution of 5 km for the period 1959–2010 by forcing MAR with different reanalysis products: ERA40/ERA-interim, NCEP/NCAR-v1, ERA-20C, and 20CRV2C. Measurements of Eg↓ from the Global Energy Balance Archive (GEBA) and from the Royal Meteorological Institute of Belgium (RMIB), as well as cloud cover observations from Belgocontrol and RMIB, were used for the evaluation of the MAR model and the forcing reanalyses. Results show that MAR enables largely reducing the mean biases that are present in the reanalyses. The trend analysis shows that only MAR forced by ERA40/ERA-interim shows historical trends, which is probably because the ERA40/ERA-interim has a better horizontal resolution and assimilates more observations than the other reanalyses that are used in this study. The results suggest that the solar brightening observed since the 1980s in Belgium has mainly been due to decreasing cloud cover.

scholarly journals Regional Climate Models Add Value to Global Model Data: A Review and Selected Examples

2011 ◽  
Vol 92 (9) ◽  
pp. 1181-1192 ◽  
Author(s):  
Frauke Feser ◽  
Burkhardt Rockel ◽  
Hans von Storch ◽  
Jörg Winterfeldt ◽  
Matthias Zahn
Keyword(s):  
Regional Climate Model ◽  
Climate Models ◽  
Climate Model ◽  
Dynamical Downscaling ◽  
Spatial Scales ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Small Scale ◽  
Model Data ◽  
Polar Lows

An important challenge in current climate modeling is to realistically describe small-scale weather statistics, such as topographic precipitation and coastal wind patterns, or regional phenomena like polar lows. Global climate models simulate atmospheric processes with increasingly higher resolutions, but still regional climate models have a lot of advantages. They consume less computation time because of their limited simulation area and thereby allow for higher resolution both in time and space as well as for longer integration times. Regional climate models can be used for dynamical down-scaling purposes because their output data can be processed to produce higher resolved atmospheric fields, allowing the representation of small-scale processes and a more detailed description of physiographic details (such as mountain ranges, coastal zones, and details of soil properties). However, does higher resolution add value when compared to global model results? Most studies implicitly assume that dynamical downscaling leads to output fields that are superior to the driving global data, but little work has been carried out to substantiate these expectations. Here a series of articles is reviewed that evaluate the benefit of dynamical downscaling by explicitly comparing results of global and regional climate model data to the observations. These studies show that the regional climate model generally performs better for the medium spatial scales, but not always for the larger spatial scales. Regional models can add value, but only for certain variables and locations—particularly those influenced by regional specifics, such as coasts, or mesoscale dynamics, such as polar lows. Therefore, the decision of whether a regional climate model simulation is required depends crucially on the scientific question being addressed.

scholarly journals Exploring Perturbed Physics Ensembles in a Regional Climate Model

2012 ◽  
Vol 25 (13) ◽  
pp. 4582-4599 ◽  
Author(s):  
Omar Bellprat ◽  
Sven Kotlarski ◽  
Daniel Lüthi ◽  
Christoph Schär
Keyword(s):  
Regional Climate Model ◽  
Annual Cycle ◽  
Cloud Cover ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Total Cloud Cover ◽  
Observational Uncertainty ◽  
Perturbed Physics ◽  
Perturbed Physics Ensembles

Abstract Perturbed physics ensembles (PPEs) have been widely used to assess climate model uncertainties and have provided new estimates of climate sensitivity and parametric uncertainty in state-of-the-art climate models. So far, mainly global climate models were used to generate PPEs, and little work has been conducted with regional climate models. This paper discusses the parameter uncertainty in two PPEs of a regional climate model driven by reanalysis data for the present climate over Europe. The uncertainty is evaluated for the variables of 2-m temperature, precipitation, and total cloud cover, with a focus on the annual cycle, interannual variability, and selected extremes. The authors show that the simulated spread of the PPEs encompasses the observations at a regional scale in terms of the annual cycle and the interannual variability, provided observational uncertainty is taken into account. To rank the PPEs a new skill metric is proposed, which takes into account observational uncertainty and natural variability. The metric is a generalization of the climate prediction index (CPI) and is compared to metrics used in other studies. The consideration of observational uncertainty is particularly important for total cloud cover and reveals that current observations do not allow for a systematic evaluation of high precipitation intensities over the entire European domain. The skill framework is additionally used to identify important model parameters, which are of interest for an objective model calibration.

scholarly journals Uncertainties in climate change projections and regional downscaling: implications for water resources management

2010 ◽  
Vol 7 (2) ◽  
pp. 1821-1848 ◽  
Author(s):  
W. Buytaert ◽  
M. Vuille ◽  
A. Dewulf ◽  
R. Urrutia ◽  
A. Karmalkar ◽  
...  
Keyword(s):  
Climate Change ◽  
Water Resources ◽  
Regional Climate Model ◽  
Water Resources Management ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Climate Change Projections ◽  
Resources Management

Abstract. Climate change is expected to have a large impact on water resources worldwide. A major problem in assessing the potential impact of a changing climate on these resources is the difference in spatial scale between available climate change projections and water resources management. Regional climate models (RCMs) are often used for the spatial disaggregation of the outputs of global circulation models. However, RCMs are time-intensive to run and typically only a small number of model runs is available for a certain region of interest. This paper investigates the value of the improved representation of local climate processes by a regional climate model for water resources management in the tropical Andes of Ecuador. This region has a complex hydrology and its water resources are under pressure. Compared to the IPCC AR4 model ensemble, the regional climate model PRECIS does indeed capture local gradients better than global models, but locally the model is prone to large discrepancies between observed and modelled precipitation. It is concluded that a further increase in resolution is necessary to represent local gradients properly. Furthermore, to assess the uncertainty in downscaling, an ensemble of regional climate models should be implemented. Finally, translating the climate variables to streamflow using a hydrological model constitutes a smaller but not negligible source of uncertainty.

scholarly journals Evaluation and Bias Correction of Regional Climate Model Results Using Model Evaluation Measures

2012 ◽  
Vol 51 (9) ◽  
pp. 1670-1684 ◽  
Author(s):  
Robert Schoetter ◽  
Peter Hoffmann ◽  
Diana Rechid ◽  
K. Heinke Schlünzen
Keyword(s):  
Wind Speed ◽  
Bias Correction ◽  
Model Evaluation ◽  
Cloud Cover ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Total Cloud Cover ◽  
Evaluation Measures

AbstractFor the assessment of regional climate change the reliability of the regional climate models needs to be known. The main goal of this paper is to evaluate the quality of climate model data that are used for impact research. Temperature, precipitation, total cloud cover, relative humidity, and wind speed simulated by the regional climate models Climate Local Model (CLM) and Regional Model (REMO) are evaluated for the metropolitan region of Hamburg in northern Germany for the period 1961–2000. The same evaluation is performed for the global climate model ECHAM5 that is used to force the regional climate models. The evaluation is based on comparison of the simulated and observed climatological annual cycles and probability density functions of daily averages. Several model evaluation measures are calculated to assure an objective model evaluation. As a very selective model evaluation measure, the hit rate of the percentiles is introduced for the evaluation of daily averages. The influence of interannual climate variability is considered by determining confidence intervals for the model evaluation measures by bootstrap resampling. Evaluation shows that, with some exceptions, temperature and wind speed are well simulated by the climate models; whereas considerable biases are found for relative humidity, total cloud cover, and precipitation, although not for all models in all seasons. It is shown that model evaluation measures can be used to decide for which meteorological parameters a bias correction is reasonable.

scholarly journals Assessment of Regional Climate Model Simulations of the Katabatic Boundary Layer Structure over Greenland

Atmosphere ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 571
Author(s):  
Günther Heinemann
Keyword(s):  
Boundary Layer ◽  
Regional Climate Model ◽  
Climate Models ◽  
Climate Model ◽  
Layer Structure ◽  
Regional Climate ◽  
Regional Climate Models ◽  
The Arctic ◽  
Temperature Cycle ◽  
Katabatic Wind

The parameterization of the boundary layer is a challenge for regional climate models of the Arctic. In particular, the stable boundary layer (SBL) over Greenland, being the main driver for substantial katabatic winds over the slopes, is simulated differently by different regional climate models or using different parameterizations of the same model. However, verification data sets with high-resolution profiles of the katabatic wind are rare. In the present paper, detailed aircraft measurements of profiles in the katabatic wind and automatic weather station data during the experiment KABEG (Katabatic wind and boundary-layer front experiment around Greenland) in April and May 1997 are used for the verification of the regional climate model COSMO-CLM (CCLM) nested in ERA-Interim reanalyses. CCLM is used in a forecast mode for the whole Arctic with 15 km resolution and is run in the standard configuration of SBL parameterization and with modified SBL parameterization. In the modified version, turbulent kinetic energy (TKE) production and the transfer coefficients for turbulent fluxes in the SBL are reduced, leading to higher stability of the SBL. This leads to a more realistic representation of the daily temperature cycle and of the SBL structure in terms of temperature and wind profiles for the lowest 200 m.

scholarly journals Regional Climate Modeling for the Developing World: The ICTP RegCM3 and RegCNET

2007 ◽  
Vol 88 (9) ◽  
pp. 1395-1410 ◽  
Author(s):  
Jeremy S. Pal ◽  
Filippo Giorgi ◽  
Xunqiang Bi ◽  
Nellie Elguindi ◽  
Fabien Solmon ◽  
...  
Keyword(s):  
Regional Climate Model ◽  
Climate Models ◽  
Climate Model ◽  
Climate Modeling ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Regional Climate Modeling ◽  
Theoretical Physics ◽  
Research Network ◽  
Diverse Range

Regional climate models are important research tools available to scientists around the world, including in economically developing nations (EDNs). The Earth Systems Physics (ESP) group of the Abdus Salam International Centre for Theoretical Physics (ICTP) maintains and distributes a state-of-the-science regional climate model called the ICTP Regional Climate Model version 3 (RegCM3), which is currently being used by a large research community for a diverse range of climate-related studies. The RegCM3 is the central, but not only, tool of the ICTP-maintained Regional Climate Research Network (RegCNET) aimed at creating south–south and north–south scientific interactions on the topic of climate and associated impacts research and modeling. In this paper, RegCNET, RegCM3, and illustrative results from RegCM3 benchmark simulations applied over south Asia, Africa, and South America are presented. It is shown that RegCM3 performs reasonably well over these regions and is therefore useful for climate studies in EDNs.

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