Climate projections in the Hornsund area, Southern Spitsbergen

Abstract The aim of this study was to provide an estimation of climate variability in the Hornsund area in Southern Spitsbergen in the period 1976-2100. The climatic variables were obtained from the Polar-CORDEX initiative in the form of time series of daily air temperature and precipitation derived from four global circulation models (GCMs) following representative concentration pathways (RCP) RCP 4.5 and RCP 8.5 emission scenarios. In the first stage of the analysis, simulations for the reference period from 1979 to 2005 were compared with observations at the Polish Polar Station Hornsund from the same period of time. In the second step, climatic projections were derived and monthly and annual means/sums were analysed as climatic indices. Following the standard methods of trend analysis, the changes of these indices over three time periods - the reference period 1976-2005, the near-future period 2021-2050, and far-future period 2071-2100 - were examined. The projections of air temperature were consistent. All analysed climate models simulated an increase of air temperature with time. Analyses of changes at a monthly scale indicated that the largest increases were estimated for winter months (more than 11°C for the far future using the RCP 8.5 scenario). The analyses of monthly and annual sums of precipitation also indicated increasing tendencies for changes with time, with the differences between mean monthly sums of precipitation for the near future and the reference period similar for each months. In the case of changes between far future and reference periods, the highest increases were projected for the winter months.

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Projection of Hydro-Climatic Extreme Events under Climate Change in Yom and Nan River Basins, Thailand

Water ◽

10.3390/w13050665 ◽

2021 ◽

Vol 13 (5) ◽

pp. 665

Author(s):

Chanchai Petpongpan ◽

Chaiwat Ekkawatpanit ◽

Supattra Visessri ◽

Duangrudee Kositgittiwong

Keyword(s):

Climate Change ◽

Air Temperature ◽

Extreme Events ◽

River Basins ◽

Annual Rainfall ◽

Extreme Floods ◽

Extreme Flood ◽

Rcp 8.5 ◽

Near Future ◽

Far Future

Due to a continuous increase in global temperature, the climate has been changing without sign of alleviation. An increase in the air temperature has caused changes in the hydrologic cycle, which have been followed by several emergencies of natural extreme events around the world. Thailand is one of the countries that has incurred a huge loss in assets and lives from the extreme flood and drought events, especially in the northern part. Therefore, the purpose of this study was to assess the hydrological regime in the Yom and Nan River basins, affected by climate change as well as the possibility of extreme floods and droughts. The hydrological processes of the study areas were generated via the physically-based hydrological model, namely the Soil and Water Assessment Tool (SWAT) model. The projected climate conditions were dependent on the outputs of the Global Climate Models (GCMs) as the Representative Concentration Pathways (RCPs) 2.6 and 8.5 between 2021 and 2095. Results show that the average air temperature, annual rainfall, and annual runoff will be significantly increased in the intermediate future (2046–2070) onwards, especially under RCP 8.5. According to the Flow Duration Curve and return period of peak discharge, there are fluctuating trends in the occurrence of extreme floods and drought events under RCP 2.6 from the future (2021–2045) to the far future (2071–2095). However, under RCP 8.5, the extreme flood and drought events seem to be more severe. The probability of extreme flood remains constant from the reference period to the near future, then rises dramatically in the intermediate and the far future. The intensity of extreme droughts will be increased in the near future and decreased in the intermediate future due to high annual rainfall, then tending to have an upward trend in the far future.

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Winter tourism and climate change in the Pyrenees and the French Alps: relevance of snowmaking as a technical adaptation

10.5194/tc-2018-253 ◽

2018 ◽

Author(s):

Pierre Spandre ◽

Hugues François ◽

Deborah Verfaillie ◽

Marc Pons ◽

Matthieu Vernay ◽

...

Keyword(s):

Climate Change ◽

Climate Projections ◽

Reference Period ◽

Winter Tourism ◽

French Alps ◽

Ski Resorts ◽

Rcp 8.5 ◽

The Alps ◽

Natural Snow ◽

Near Future

Abstract. Climate change is increasingly regarded as a threat for winter tourism due to the combined effect of decreasing natural snow amounts and decreasing suitable periods for snowmaking. The present work investigated the snow reliability of 175 ski resorts in France (Alps and Pyrenees), Spain and Andorra under past and future conditions using state-of-the-art snowpack modelling and climate projections. The natural snow reliability (i.e. without snowmaking) elevation showed a significant spatial variability in the reference period (1986–2005) and to be highly impacted by the on-going climate change. The technical reliability (i.e. including snowmaking) is projected to rise by 200 m to 300 m in the Alps and by 400 m to 600 m in the Pyrenees in the near future (2030–2050) compared to the reference period for all climate scenarios. While 99 % of ski lift infrastructures are reliable in the reference period thanks to snowmaking, a significant fraction (14 % to 25 %) may be considered "at risk" in the near future. Beyond the mid century, climate projections highly depend on the scenario with steady conditions compared to the near future (RCP 2.6) or continuous decrease of snow reliability (RCP 8.5). According to the "business as usual" scenario (RCP 8.5), there would no longer be any snow reliable ski resorts based on natural snow conditions in French Alps and Pyrenees (France, Spain and Andorra) at the end of the century (2080–2100). Only 24 resorts are projected to remain technically reliable, all being located in the Alps.

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Impact of Climate Change on the Hydrological Regimes in Bavaria

Water ◽

10.3390/w12061599 ◽

2020 ◽

Vol 12 (6) ◽

pp. 1599 ◽

Cited By ~ 1

Author(s):

Benjamin Poschlod ◽

Florian Willkofer ◽

Ralf Ludwig

Keyword(s):

Climate Change ◽

Climate Model ◽

Emission Scenario ◽

Regional Climate ◽

Hierarchical Cluster ◽

Reference Period ◽

Rcp 8.5 ◽

Monthly Discharge ◽

Near Future ◽

Far Future

This study assesses the change of the seasonal runoff characteristics in 98 catchments in central Europe between the reference period of 1981–2010, and in the near future (2011–2040), mid future (2041–2070) and far future (2071–2099). Therefore, a large ensemble of 50 hydrological simulations featuring the model WaSiM-ETH driven by a 50-member ensemble of the Canadian Regional Climate Model, version 5 (CRCM5) under the emission scenario Representative Concentration Pathway (RCP 8.5) is analyzed. A hierarchical cluster analysis is applied to group the runoff characteristics into six flow regime classes. In the study area, (glacio-)nival, nival (transition), nivo-pluvial and three different pluvial classes are identified. We find that the characteristics of all six regime groups are severely affected by climate change in terms of the amplitude and timing of the monthly peaks and sinks. According to our simulations, the monthly peak of nival regimes will occur earlier in the season and the relative importance of rainfall increases towards the future. Pluvial regimes will become less balanced with higher normalized monthly discharge during January to March and a strong decrease during May to October. In comparison to the reference period, 8% of catchments will shift to another regime class until 2011–2040, whereas until 2041–2070 and 2071–2099, 23% and 43% will shift to another class, respectively.

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Future Projection of Mean Temperature of Post-Monsoon Season Over Bangladesh Using Statistical Downscaling of Global Climate Models

Journal of Engineering Science ◽

10.3329/jes.v11i2.50895 ◽

2020 ◽

Vol 11 (2) ◽

pp. 27-35

Author(s):

Md Bazlur Rashid ◽

Syed Shahadat Hossain

Keyword(s):

Statistical Downscaling ◽

Climate Models ◽

Monsoon Season ◽

Future Climate ◽

Climate Projections ◽

Post Monsoon ◽

Mean Temperature ◽

Post Monsoon Season ◽

Near Future ◽

Far Future

In statistical downscaling technique, regional or local information are derived by determining a statistical model which relates large-scale climate variables or predictors generated by Global Climate Models (GCMs) to regional and local variables or predictands. In this paper, the results of GCMs were statistically downscaled to produce future climate projections of mean temperature in the post-monsoon season (October and November), for the time periods 2021-2050 and 2071-2100 for Bangladesh. The future climate projections are based on the three emission scenarios RCP2.6, RCP4.5 and RCP8.5 provided by the fifth Coupled Model Intercomparison Project (CMIP5). This paper established a method to analyze GCMs for use in statistical downscaling and utilized fifteen GCMs. The GCMs were assessed based upon their performance in simulated past climate in Bangladesh and adjoining areas. Downscaling was undertaken by linking large scale climate variables, taken from the ERA-Interim (resolution 79 km) reanalysis temperature, a gridded data set incorporating observations and climate models, to local scale observations. Overall, all fifteen GCMs, via statistical downscaling, show that mean temperature of the post-monsoon season in Bangladesh will increase under future climate scenarios. Comparing the ensemble of future projections with the reference period (1981- 2010), the mean post-monsoon temperature in Bangladesh is projected for RCP8.5 showing warming by 0.310C in near future and 1.790C in far future. On the other hand, estimated warming is 0.390C in near future and 1.140C is far future for RCP4.5. Low emission scenarios RCP2.6, near future temperature is nearly same the far future temperature. Journal of Engineering Science 11(2), 2020, 27-35

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Climate Projections for South America: RegCM3 Driven by HadCM3 and ECHAM5

Advances in Meteorology ◽

10.1155/2014/376738 ◽

2014 ◽

Vol 2014 ◽

pp. 1-17 ◽

Cited By ~ 22

Author(s):

Michelle Simões Reboita ◽

Rosmeri Porfírio da Rocha ◽

Cássia Gabriele Dias ◽

Rita Yuri Ynoue

Keyword(s):

South America ◽

Climate Model ◽

Regional Climate ◽

Climate Projections ◽

South American ◽

Intergovernmental Panel ◽

Temperature And Precipitation ◽

Precipitation Increase ◽

Near Future ◽

Far Future

This study shows climate projections of air temperature and precipitation over South America (SA) from the Regional Climate Model version 3 (RegCM3) nested in ECHAM5 and HadCM3 global models. The projections consider the A1B scenario from Intergovernmental Panel on Climate Change (IPCC) and three time-slices: present (1960–1990), near- (2010–2040), and far-future (2070–2100) climates. In the future, RegCM3 projections indicate general warming throughout all SA and seasons, which is more pronounced in the far-future period. In this late period the RegCM3 projections indicate that the negative trend of precipitation over northern SA is also higher. In addition, a precipitation increase over southeastern SA is projected, mainly during summer and spring. The lifecycle of the South American monsoon (SAM) was also investigated in the present and future climates. In the near-future, the projections show a slight delay (one pentad) of the beginning of the rainy season, resulting in a small reduction of the SAM length. In the far-future, there is no agreement between projections related to the SAM features.

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Climate Change Influences of Temporal and Spatial Drought Variation in the Andean High Mountain Basin

Atmosphere ◽

10.3390/atmos10090558 ◽

2019 ◽

Vol 10 (9) ◽

pp. 558 ◽

Cited By ~ 1

Author(s):

Dario Zhiña ◽

Martín Montenegro ◽

Lisseth Montalván ◽

Daniel Mendoza ◽

Juan Contreras ◽

...

Keyword(s):

Climate Change ◽

Climate Models ◽

Global Climate ◽

Climate Impacts ◽

Global Climate Models ◽

Return Periods ◽

Base Period ◽

Rcp 8.5 ◽

Near Future ◽

Far Future

Climate change threatens the hydrological equilibrium with severe consequences for living beings. In that respect, considerable differences in drought features are expected, especially for mountain-Andean regions, which seem to be prone to climate change. Therefore, an urgent need for evaluation of such climate conditions arises; especially the effects at catchment scales, due to its implications over the hydrological services. However, to study future climate impacts at the catchment scale, the use of dynamically downscaled data in developing countries is a luxury due to the computational constraints. This study performed spatiotemporal future long-term projections of droughts in the upper part of the Paute River basin, located in the southern Andes of Ecuador. Using 10 km dynamically downscaled data from four global climate models, the standardized precipitation and evapotranspiration index (SPEI) index was used for drought characterization in the base period (1981–2005) and future period (2011–2070) for RCP 4.5 and RCP 8.5 of CMIP5 project. Fitting a generalized-extreme-value (GEV) distribution, the change ratio of the magnitude, duration, and severity between the future and present was evaluated for return periods 10, 50, and 100 years. The results show that magnitude and duration dramatically decrease in the near future for the climate scenarios under analysis; these features presented a declining effect from the near to the far future. Additionally, the severity shows a general increment with respect to the base period, which is intensified with longer return periods; however, the severity shows a decrement for specific areas in the far future of RCP 4.5 and near future of RCP 8.5. This research adds knowledge to the evaluation of droughts in complex terrain in tropical regions, where the representation of convection is the main limitation of global climate models (GCMs). The results provide useful information for decision-makers supporting mitigating measures in future decades.

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High-Resolution Climate Projections for a Densely Populated Mediterranean Region

Sustainability ◽

10.3390/su12093684 ◽

2020 ◽

Vol 12 (9) ◽

pp. 3684

Author(s):

Mohamed Salem Nashwan ◽

Shamsuddin Shahid ◽

Eun-Sung Chung

Keyword(s):

Climate Change ◽

High Resolution ◽

Bias Correction ◽

Climate Models ◽

Global Climate ◽

Global Climate Models ◽

Future Climate Change ◽

Climate Projections ◽

Near Future ◽

Far Future

The present study projected future climate change for the densely populated Central North region of Egypt (CNE) for two representative concentration pathways (RCPs) and two futures (near future: 2020–2059, and far future: 2060–2099), estimated by a credible subset of five global climate models (GCMs). Different bias correction models have been applied to correct the bias in the five interpolated GCMs’ outputs onto a high-resolution horizontal grid. The 0.05° CNE datasets of maximum and minimum temperatures (Tmx, and Tmn, respectively) and the 0.1° African Rainfall Climatology (ARC2) datasets represented the historical climate. The evaluation of bias correction methodologies revealed the better performance of linear and variance scaling for correcting the rainfall and temperature GCMs’ outputs, respectively. They were used to transfer the correction factor to the projections. The five statistically bias-corrected climate projections presented the uncertainty range in the future change in the climate of CNE. The rainfall is expected to increase in the near future but drastically decrease in the far future. The Tmx and Tmn are projected to increase in both future periods reaching nearly a maximum of 5.50 and 8.50 °C for Tmx and Tmn, respectively. These findings highlighted the severe consequence of climate change on the socio-economic activities in the CNE aiming for better sustainable development.

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A high-resolution downscaled CMIP6 projections dataset of essential surface climate variables over the globe coherent with the ERA5-Land reanalysis for climate change impact assessments

10.31223/x5s610 ◽

2021 ◽

Author(s):

Thomas Noël ◽

Harilaos Loukos ◽

Dimitri Defrance

Keyword(s):

Climate Change ◽

High Resolution ◽

Climate Models ◽

Mapping Method ◽

Cumulative Distribution ◽

Climate Projections ◽

Climate Modelling ◽

Temperature And Precipitation ◽

Surface Climate ◽

Emissions Scenarios

A high-resolution climate projections dataset is obtained by statistically downscaling climate projections from the CMIP6 experiment using the ERA5-Land reanalysis from the Copernicus Climate Change Service. This global dataset has a spatial resolution of 0.1°x 0.1°, comprises 5 climate models and includes two surface daily variables at monthly resolution: air temperature and precipitation. Two greenhouse gas emissions scenarios are available: one with mitigation policy (SSP126) and one without mitigation (SSP585). The downscaling method is a Quantile Mapping method (QM) called the Cumulative Distribution Function transform (CDF-t) method that was first used for wind values and is now referenced in dozens of peer-reviewed publications. The data processing includes quality control of metadata according to the climate modelling community standards and value checking for outlier detection.

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PEMANFAATAN MODEL PROYEKSI IKLIM DAN SIMULASI TANAMAN DALAM PENGUATAN ADAPTASI SISTEM PERTANIAN PADI TERHADAP PENURUNAN PRODUKTIVITAS AKIBAT PERUBAHAN IKLIM: STUDI KASUS DI KABUPATEN SUMEDANG, JAWA BARAT

Informatika Pertanian ◽

10.21082/ip.v23n2.2014.p159-168 ◽

2016 ◽

Vol 23 (2) ◽

pp. 159

Author(s):

Candradijaya A

Keyword(s):

Climate Change ◽

General Circulation ◽

Climate Models ◽

Rice Yield ◽

Local Level ◽

Irrigation Scheduling ◽

Yield Reduction ◽

Planting Time ◽

Near Future ◽

Far Future

Despite the well-documented model-simulated adverse climate change impact on rice yields reported elsewhere, interventions to address the issue seem to be still limited, particularly at local level. This links to the uncertainty that entails to climate projection and its likely future impact, which varies across regions and climate models. The study analyzes climate change-induced rice yield reduction and the adequacy of current adaptations, to cope with a large range of impact under various climate models. Seventeen General Circulation Models (GCMs) under Representative Concentration Pathways (RCPs) climate change with scenarios of RCP8.5 and RCP4.5, combined with CROPWAT model for near-future (2011-2040) and far-future (2041-2070) projections. The study was conducted in November-December 2013, in Ujungjaya Subdistrict, the District of Sumedang. The output confirms yield reduction to occur in the near-future, to the extent variable across the GCMs. At the highest estimation, rice yield decreases by 32.00% and 31.81%, in comparison to baseline, for near-future under RCP8.5 and RCP4.5, respectively. The reduction extends, with a slightly higher degree, to the far-future. The reduction is sensitive to variation in farming practices of the local farmers, in particular that in planting time and irrigation scheduling. The shifting of planting time to better match rainfall pattern reduces the rice yield by 12.95% for rainfed and 14.07% for the irrigated farming. Meanwhile, improved irrigation scheduling reduces the yield reduction by 16.16%. The findings provide valuable inputs for relevant authorities to understand the climate change-induced rice yield reduction, and to formalate intervention strategies for spesific-location adaptation.

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Diagnosis of Multiyear Predictability on Continental Scales

Journal of Climate ◽

10.1175/2011jcli4098.1 ◽

2011 ◽

Vol 24 (19) ◽

pp. 5108-5124 ◽

Cited By ~ 20

Author(s):

Liwei Jia ◽

Timothy DelSole

Keyword(s):

Time Scales ◽

Air Temperature ◽

Climate Models ◽

Coupled Model ◽

Surface Air Temperature ◽

Optimization Method ◽

First Year ◽

Temperature And Precipitation ◽

Regional Prediction ◽

Intercomparison Project

A new statistical optimization method is used to identify components of surface air temperature and precipitation on six continents that are predictable in multiple climate models on multiyear time scales. The components are identified from unforced “control runs” of the Coupled Model Intercomparison Project phase 3 dataset. The leading predictable components can be calculated in independent control runs with statistically significant skill for 3–6 yr for surface air temperature and 1–3 yr for precipitation, depending on the continent, using a linear regression model with global sea surface temperature (SST) as a predictor. Typically, lag-correlation maps reveal that the leading predictable components of surface air temperature are related to two types of SST patterns: persistent patterns near the continent itself and an oscillatory ENSO-like pattern. The only exception is Europe, which has no significant ENSO relation. The leading predictable components of precipitation are significantly correlated with an ENSO-like SST pattern. No multiyear predictability of land precipitation could be verified in Europe. The squared multiple correlations of surface air temperature and precipitation for nonzero lags on each continent are less than 0.4 in the first year, implying that less than 40% of variations of the leading predictable component can be predicted from global SST. The predictable components describe the spatial structures that can be predicted on multiyear time scales in the absence of anthropogenic and natural forcing, and thus provide a scientific rationale for regional prediction on multiyear time scales.

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