scholarly journals Projected climate change in Australian marine and freshwater environments

2011 ◽  
Vol 62 (9) ◽  
pp. 1000 ◽  
Author(s):  
Alistair J. Hobday ◽  
Janice M. Lough
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Spatial Scales ◽  
Global Climate Models ◽  
Climate Projections ◽  
Western Coast ◽  
Freshwater Species ◽  
Freshwater Systems ◽  
Eastern Australia ◽  
Non Linear System

Changes in the physical environment of aquatic systems consistent with climate change have been reported across Australia, with impacts on many marine and freshwater species. The future state of aquatic environments can be estimated by extrapolation of historical trends. However, because the climate is a complex non-linear system, a more process-based approach is probably required, in particular the use of dynamical projections using climate models. Because global climate models operate on spatial scales that typically are too coarse for aquatic biologists, statistical or dynamical downscaling of model output is proposed. Challenges in using climate projections exist; however, projections for some marine and freshwater systems are possible. Higher oceanic temperatures are projected around Australia, particularly for south-eastern Australia. The East Australia Current is projected to transport greater volumes of water southward, whereas the Leeuwin Current on the western coast may weaken. On land, projections suggest that air temperatures will rise and rainfall will decline across much of Australia in coming decades. Together, these changes will result in reduced runoff and hence reduced stream flow and lake storage. Present climate models are particularly limited with regard to coastal and freshwater systems, making the models challenging to use for biological-impact and adaptation studies.

Realised added value in dynamical downscaling of Australian climate change

2021 ◽  
Author(s):  
Giovanni Di Virgilio ◽  
Jason P. Evans ◽  
Alejandro Di Luca ◽  
Michael R. Grose ◽  
Vanessa Round ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Climate Adaptation ◽  
Regional Climate ◽  
Future Climate ◽  
Added Value ◽  
Climate Projections ◽  
Eastern Australia ◽  
Australian Alps ◽  
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, ‘realised added value’, 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>

scholarly journals Design of a regional climate modelling projection ensemble experiment – NARCliM

2014 ◽  
Vol 7 (2) ◽  
pp. 621-629 ◽  
Author(s):  
J. P. Evans ◽  
F. Ji ◽  
C. Lee ◽  
P. Smith ◽  
D. Argüeso ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Selection Process ◽  
Regional Climate ◽  
Global Climate Models ◽  
Regional Climate Modelling ◽  
Future Research ◽  
Climate Projections ◽  
Climate Modelling ◽  
Future Change

Abstract. Including the impacts of climate change in decision making and planning processes is a challenge facing many regional governments including the New South Wales (NSW) and Australian Capital Territory (ACT) governments in Australia. NARCliM (NSW/ACT Regional Climate Modelling project) is a regional climate modelling project that aims to provide a comprehensive and consistent set of climate projections that can be used by all relevant government departments when considering climate change. To maximise end user engagement and ensure outputs are relevant to the planning process, a series of stakeholder workshops were run to define key aspects of the model experiment including spatial resolution, time slices, and output variables. As with all such experiments, practical considerations limit the number of ensemble members that can be simulated such that choices must be made concerning which global climate models (GCMs) to downscale from, and which regional climate models (RCMs) to downscale with. Here a methodology for making these choices is proposed that aims to sample the uncertainty in both GCM and RCM ensembles, as well as spanning the range of future climate projections present in the GCM ensemble. The RCM selection process uses performance evaluation metrics to eliminate poor performing models from consideration, followed by explicit consideration of model independence in order to retain as much information as possible in a small model subset. In addition to these two steps the GCM selection process also considers the future change in temperature and precipitation projected by each GCM. The final GCM selection is based on a subjective consideration of the GCM independence and future change. The created ensemble provides a more robust view of future regional climate changes. Future research is required to determine objective criteria that could replace the subjective aspects of the selection process.

scholarly journals Statistically Downscaled CMIP6 Projections Show Stronger Warming for Germany

Atmosphere ◽  
2020 ◽  
Vol 11 (11) ◽  
pp. 1245
Author(s):  
Frank Kreienkamp ◽  
Philip Lorenz ◽  
Tobias Geiger
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Global Climate ◽  
Regional Scale ◽  
Coupled Model ◽  
Global Climate Models ◽  
Climate Projections ◽  
Climate Modelling ◽  
Climate Signal ◽  
Temperature Signal

Climate modelling output that was provided under the latest Coupled Model Intercomparison Project (CMIP6) shows significant changes in model-specific Equilibrium Climate Sensitivity (ECS) as compared to CMIP5. The newer versions of many Global Climate Models (GCMs) report higher ECS values that result in stronger global warming than previously estimated. At the same time, the multi-GCM spread of ECS is significantly larger than under CMIP5. Here, we analyse how the differences between CMIP5 and CMIP6 affect climate projections for Germany. We use the statistical-empirical downscaling method EPISODES in order to downscale GCM data for the scenario pairs RCP4.5/SSP2-4.5 and RCP8.5/SSP5-8.5. We use data sets of the GCMs CanESM, EC-Earth, MPI-ESM, and NorESM. The results show that the GCM-specific changes in the ECS also have an impact at the regional scale. While the temperature signal under regional climate change remains comparable for both CMIP generations in the MPI-ESM chain, the temperature signal increases by up to 3 °C for the RCP8.5/SSP5-8.5 scenario pair in the EC-Earth chain. Changes in precipitation are less pronounced and they only show notable differences at the seasonal scale. The reported changes in the climate signal will have direct consequences for society. Climate change impacts previously projected for the high-emission RCP8.5 scenario might occur equally under the new SSP2-4.5 scenario.

scholarly journals Estimating the potential impact of climate change on sunflower yield in the Konya province of Turkey

2021 ◽  
pp. 1-13
Author(s):  
Hudaverdi Gurkan ◽  
Vakhtang Shelia ◽  
Nilgun Bayraktar ◽  
Y. Ersoy Yildirim ◽  
Nebi Yesilekin ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Global Climate ◽  
Model Performance ◽  
Global Climate Models ◽  
Climate Projections ◽  
Climate Change Scenarios ◽  
Adaptation To Climate Change ◽  
Impact Of Climate Change ◽  
The Impact

Abstract The impact of climate change on agricultural productivity is difficult to assess. However, determining the possible effects of climate change is an absolute necessity for planning by decision-makers. The aim of the study was the evaluation of the CSM-CROPGRO-Sunflower model of DSSAT4.7 and the assessment of impact of climate change on sunflower yield under future climate projections. For this purpose, a 2-year sunflower field experiment was conducted under semi-arid conditions in the Konya province of Turkey. Rainfed and irrigated treatments were used for model analysis. For the assessment of impact of climate change, three global climate models and two representative concentration pathways, i.e. 4.5 and 8.5 were selected. The evaluation of the model showed that the model was able to simulate yield reasonably well, with normalized root mean square error of 1.3% for the irrigated treatment and 17.7% for the rainfed treatment, a d-index of 0.98 and a modelling efficiency of 0.93 for the overall model performance. For the climate change scenarios, the model predicted that yield will decrease in a range of 2.9–39.6% under rainfed conditions and will increase in a range of 7.4–38.5% under irrigated conditions. Results suggest that temperature increases due to climate change will cause a shortening of plant growth cycles. Projection results also confirmed that increasing temperatures due to climate change will cause an increase in sunflower water requirements in the future. Thus, the results reveal the necessity to apply adequate water management strategies for adaptation to climate change for sunflower production.

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 A regional climate modelling projection ensemble experiment – NARCliM

2013 ◽  
Vol 6 (3) ◽  
pp. 5117-5139 ◽  
Author(s):  
J. P. Evans ◽  
F. Ji ◽  
C. Lee ◽  
P. Smith ◽  
D. Argüeso ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Planning Process ◽  
Global Climate ◽  
Regional Climate ◽  
Global Climate Models ◽  
Regional Climate Modelling ◽  
Climate Projections ◽  
Climate Modelling ◽  
User Engagement

Abstract. Including the impacts of climate change in decision making and planning processes is a challenge facing many regional governments including the New South Wales (NSW) and Australian Capital Territory (ACT) governments in Australia. NARCliM (NSW/ACT Regional Climate Modelling project) is a regional climate modelling project that aims to provide a comprehensive and consistent set of climate projections that can be used by all relevant government departments when considering climate change. To maximise end user engagement and ensure outputs are relevant to the planning process, a series of stakeholder workshops were run to define key aspects of the model experiment including spatial resolution, time slices, and output variables. As with all such experiments, practical considerations limit the number of ensembles members that can be simulated such that choices must be made concerning which Global Climate Models (GCMs) to downscale from, and which Regional Climate Models (RCMs) to downscale with. Here a methodology for making these choices is proposed that aims to sample the uncertainty in both GCMs and RCMs, as well as spanning the range of future climate projections present in the full GCM ensemble. The created ensemble provides a more robust view of future regional climate changes.

scholarly journals Uncertainty of Streamflow Forecasting with the Climate Change Scenario in India

2020 ◽  
Vol 1 (3) ◽  
Author(s):  
Ankit Bhatt ◽  
Ajay Pradhan
Keyword(s):  
Climate Change ◽  
Large Scale ◽  
Climate Change Scenario ◽  
Climate Models ◽  
Spatial Scales ◽  
Global Climate Models ◽  
Seasonal Effects ◽  
Change Scenario ◽  
Streamflow Forecasting ◽  
Projected Changes

Streamflow and rainfall estimates have utmost importance to compute detailed water availability and hydrology for many sectors such as agriculture, water management, and food security. There are various models developed over the years for runoff estimation but among them only a few models incorporate climate change factors. Snowmelt and rainfall are the main sources of surface as well as groundwater resource and the main inputs in runoff models for estimation of streamflow. There are numerous factors which leads to climate change which intern affects the distribution on rainfall on spatial and temporal scales and the rate of melting of snows in the Himalayan region. Uncertainties in projected changes in the hydrological systems arise from internal variability in the climatic system, uncertainty about future greenhouse gas and aerosol emissions, the translations of these emissions into climate change by global climate models, and hydrological model uncertainty. Projections become less consistent between models as the spatial scale decreases. The uncertainty of climate model projections for freshwater assessments is often taken into account by using multi-model ensembles. The multi-model ensemble approach is, however, not a guarantee of reducing uncertainty in mathematical models. In recent years the floods have occurred due to high intensity rainfall occurred in a very short time, but in several cases the flooding has also occurred because the rainfall has fallen at times when all the storage systems have not been emptied after the previous rainfall. This is what we call coupled rainfall. There is currently no recommendation for how to take coupled rainfall account when applying the climate change scenario. It is estimated that such changes represent at a large scale, and cannot be applied to shorter temporal and smaller spatial scales. In areas where rainfall and runoff are very low (e.g., desert areas), small changes in runoff can lead to large percentage changes. In some regions, the sign of projected changes in runoff differs from recently observed trends. Moreover, in some areas with projected increases in runoff, different seasonal effects are expected, such as increased wet season runoff and decreased dry season runoff. Studies using results from fewer climate models can be considerably different from the other models

scholarly journals Dry Spells in Croatia: Observed Climate Change and Climate Projections

Atmosphere ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 652
Author(s):  
Ivana Marinović ◽  
Ksenija Cindrić Kalin ◽  
Ivan Güttler ◽  
Zoran Pasarić
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Daily Precipitation ◽  
Global Climate Models ◽  
Climate Change Signal ◽  
Climate Projections ◽  
Model Errors ◽  
Dry Spells ◽  
Daily Precipitation Data ◽  
Increasing Trend

This study performs a systematic analysis of the recent and future changes of dry spells (DS) in Croatia. DS are defined as consecutive sequences of days with daily precipitation less than 5 mm of the precipitation-per-day threshold (DS5). Daily precipitation data come from a dense national rain gauge network (covering seven regions) and span the period 1961–2015. The spatial and temporal changes of the observed mean (MDS5) and maximum (MxDS5) seasonal and annual dry spells were analysed by means of the Kendall tau method and the partial trend method. Future changes of DS5 were assessed by employing the three regional climate models (RegCM4, CCLM4 and RCA4) covering the EURO-CORDEX domain with a 12.5-km horizontal resolution, resulting in a realistic orography and land–sea border over Croatia. The models were forced at their boundaries by the four CMIP5 global climate models. For the reference period 1971–2000, the observed, as well as modelled, DS5 were analysed, and the systematic model errors were assessed. Finally, the projections and future changes of the DS5 statistics based on simulations under the high and medium greenhouse gases concentration scenarios (i.e., RCP8.5 and RCP4.5) with a focus on the climate change signal between 1971–2000 and two future periods, 2011–2040 and 2041–2070, were examined. A prevailing increasing trend of MDS5 was found in the warm part of the year, being significant in the mountainous littoral and North Adriatic coastal region. An increasing trend of MxDS5 was also found in the warm part of the year (both the spring and summer), and it was particularly pronounced along the Adriatic coast, while a coherent negative trend pattern was found in the autumn. By applying the partial trend methodology, an increase was found in the very long DS5 (above the 90th percentile) in the recent half of the analysed 55-year period in all seasons, except in the autumn when shortening in the DS5 was detected. The climate change signal during the two analysed future periods was positive for the summer in all regions, weakly negative for the winter and not conclusive for the spring, autumn and year. It was found that no RCM-GCM combination is the best in all cases, since the most successful model combinations depend on the season and location.

scholarly journals High-Resolution Climate Projections for a Densely Populated Mediterranean Region

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.

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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