Dry Spells in Croatia: Observed Climate Change and Climate Projections

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.

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Climate Model Biases and Modification of the Climate Change Signal by Intensity-Dependent Bias Correction

Journal of Climate ◽

10.1175/jcli-d-17-0765.1 ◽

2018 ◽

Vol 31 (16) ◽

pp. 6591-6610 ◽

Cited By ~ 13

Author(s):

Martin Aleksandrov Ivanov ◽

Jürg Luterbacher ◽

Sven Kotlarski

Keyword(s):

Climate Change ◽

Bias Correction ◽

Climate Models ◽

Climate Model ◽

Daily Precipitation ◽

Analytical Description ◽

Analytical Theory ◽

Climate Change Signal ◽

Future Research ◽

Model Biases

Climate change impact research and risk assessment require accurate estimates of the climate change signal (CCS). Raw climate model data include systematic biases that affect the CCS of high-impact variables such as daily precipitation and wind speed. This paper presents a novel, general, and extensible analytical theory of the effect of these biases on the CCS of the distribution mean and quantiles. The theory reveals that misrepresented model intensities and probability of nonzero (positive) events have the potential to distort raw model CCS estimates. We test the analytical description in a challenging application of bias correction and downscaling to daily precipitation over alpine terrain, where the output of 15 regional climate models (RCMs) is reduced to local weather stations. The theoretically predicted CCS modification well approximates the modification by the bias correction method, even for the station–RCM combinations with the largest absolute modifications. These results demonstrate that the CCS modification by bias correction is a direct consequence of removing model biases. Therefore, provided that application of intensity-dependent bias correction is scientifically appropriate, the CCS modification should be a desirable effect. The analytical theory can be used as a tool to 1) detect model biases with high potential to distort the CCS and 2) efficiently generate novel, improved CCS datasets. The latter are highly relevant for the development of appropriate climate change adaptation, mitigation, and resilience strategies. Future research needs to focus on developing process-based bias corrections that depend on simulated intensities rather than preserving the raw model CCS.

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Subsampling Impact on the Climate Change Signal over Poland Based on Simulations from Statistical and Dynamical Downscaling

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-18-0179.1 ◽

2019 ◽

Vol 58 (5) ◽

pp. 1061-1078 ◽

Cited By ~ 8

Author(s):

Abdelkader Mezghani ◽

Andreas Dobler ◽

Rasmus Benestad ◽

Jan Erik Haugen ◽

Kajsa M. Parding ◽

...

Keyword(s):

Climate Change ◽

Climate Models ◽

Climate Model ◽

Emission Scenario ◽

Dynamical Downscaling ◽

Full Range ◽

Global Climate Models ◽

Climate Change Signal ◽

High Emission ◽

Selection Of

ABSTRACTMost impact studies using downscaled climate data as input assume that the selection of few global climate models (GCMs) representing the largest spread covers the likely range of future changes. This study shows that including more GCMs can result in a very different behavior. We tested the influence of selecting various subsets of GCMs on the climate change signal over Poland from simulations based on dynamical and empirical–statistical downscaling methods. When the climate variable is well simulated by the GCM, such as temperature, results showed that both downscaling methods agree on a warming over Poland by up to 2° or 5°C assuming intermediate or high emission scenarios, respectively, by 2071–2100. As a less robust simulated signal through GCMs, precipitation is expected to increase by up to 10% by 2071–2100 assuming the intermediate emission scenario. However, these changes are uncertain when the high emission scenario and the end of the twenty-first century are of interest. Further, an additional bootstrap test revealed an underestimation in the warming rate varying from 0.5° to more than 4°C over Poland that was found to be largely influenced by the selection of few driving GCMs instead of considering the full range of possible climate model outlooks. Furthermore, we found that differences between various combinations of small subsets from the GCM ensemble of opportunities can be as large as the climate change signal.

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Projected climate change in Australian marine and freshwater environments

Marine and Freshwater Research ◽

10.1071/mf10302 ◽

2011 ◽

Vol 62 (9) ◽

pp. 1000 ◽

Cited By ~ 191

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.

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Design of a regional climate modelling projection ensemble experiment – NARCliM

Geoscientific Model Development ◽

10.5194/gmd-7-621-2014 ◽

2014 ◽

Vol 7 (2) ◽

pp. 621-629 ◽

Cited By ~ 112

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.

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High-Resolution Climate Change Impact Analysis on Medium-Sized River Catchments in Germany: An Ensemble Assessment

Journal of Hydrometeorology ◽

10.1175/jhm-d-12-091.1 ◽

2013 ◽

Vol 14 (4) ◽

pp. 1175-1193 ◽

Cited By ~ 36

Author(s):

Irena Ott ◽

Doris Duethmann ◽

Joachim Liebert ◽

Peter Berg ◽

Hendrik Feldmann ◽

...

Keyword(s):

Climate Change ◽

High Resolution ◽

Impact Analysis ◽

Climate Models ◽

Global Climate Models ◽

Climate Change Signal ◽

Climate Modelling ◽

Community Land Model ◽

The Impact ◽

River Catchments

Abstract The impact of climate change on three small- to medium-sized river catchments (Ammer, Mulde, and Ruhr) in Germany is investigated for the near future (2021–50) following the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) A1B scenario. A 10-member ensemble of hydrological model (HM) simulations, based on two high-resolution regional climate models (RCMs) driven by two global climate models (GCMs), with three realizations of ECHAM5 (E5) and one realization of the Canadian Centre for Climate Modelling and Analysis version 3 (CCCma3; C3) is established. All GCM simulations are downscaled by the RCM Community Land Model (CLM), and one realization of E5 is downscaled also with the RCM Weather Research and Forecasting Model (WRF). This concerted 7-km, high-resolution RCM ensemble provides a sound basis for runoff simulations of small catchments and is currently unique for Germany. The hydrology for each catchment is simulated in an overlapping scheme, with two of the three HMs used in the project. The resulting ensemble hence contains for each chain link (GCM–realization–RCM–HM) at least two members and allows the investigation of qualitative and limited quantitative indications of the existence and uncertainty range of the change signal. The ensemble spread in the climate change signal is large and varies with catchment and season, and the results show that most of the uncertainty of the change signal arises from the natural variability in winter and from the RCMs in summer.

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Capacity of climate models to capture multi-decadal hydrological variations over France

10.5194/egusphere-egu2020-3525 ◽

2020 ◽

Author(s):

Julien Boé ◽

Rémy Bonnet

Keyword(s):

Climate Change ◽

Climate Models ◽

Global Climate ◽

Global Climate Models ◽

Climate Change Signal ◽

River Flows ◽

Climate Reconstructions ◽

Decadal Variations ◽

Instrumental Period

In France, large multi-decadal variations in river flows have occurred over the instrumental period. These multi-decadal variations, likely of internal origin, could be a major source of uncertainties in the evolution of river flows during the 21st century, and especially during the coming decades, when the climate change signal is weaker. Depending on their phase, these variations might indeed strongly temporarily amplify or weaken (and even possibly reverse) the signal of climate change.&#160;From an adaptation perspective, it is crucial that hydrological projections correctly capture the amplitude of these multi-decadal variations, so that the associated uncertainties can be correctly estimated.&#160;The realism of hydrological projections in this context lies to a large extent in the realism of climate models, used at the first stage of the vast majority of the studies of the impacts of climate change.The brevity of the instrumental record makes it difficult to characterize robustly multi-decadal hydro-climate variations, and the lack of observations for important hydrological variables makes it difficult to understand the mechanisms at play. The evaluation of climate models in this context is therefore also particularly challenging.&#160;In this presentation, I will describe our work to better characterize hydrological variations over France in terms of amplitude and mechanisms, thanks to joint use of newly developed hydrological reconstructions beginning in the mid-nineteenth century, long observations from data-rescue efforts and paleo-climate reconstructions. Based on this work, I will then describe the results of the evaluation of multi-decadal hydrological variations in current global climate models, in terms of amplitude and associated mechanisms, taking into account the very large sampling uncertainties associated with the characterization of multi-decadal variations on relatively short periods.&#160;

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Statistically Downscaled CMIP6 Projections Show Stronger Warming for Germany

Atmosphere ◽

10.3390/atmos11111245 ◽

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.

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A Hybrid Dynamical–Statistical Downscaling Technique. Part II: End-of-Century Warming Projections Predict a New Climate State in the Los Angeles Region

Journal of Climate ◽

10.1175/jcli-d-14-00197.1 ◽

2015 ◽

Vol 28 (12) ◽

pp. 4618-4636 ◽

Cited By ~ 24

Author(s):

Fengpeng Sun ◽

Daniel B. Walton ◽

Alex Hall

Keyword(s):

Climate Change ◽

Los Angeles ◽

Climate Models ◽

Greenhouse Gas Emission ◽

Regional Scale ◽

Global Climate Models ◽

First Century ◽

Climate Change Signal ◽

Uncertainty Estimates ◽

Twenty First Century

Abstract Using the hybrid downscaling technique developed in part I of this study, temperature changes relative to a baseline period (1981–2000) in the greater Los Angeles region are downscaled for two future time slices: midcentury (2041–60) and end of century (2081–2100). Two representative concentration pathways (RCPs) are considered, corresponding to greenhouse gas emission reductions over coming decades (RCP2.6) and to continued twenty-first-century emissions increases (RCP8.5). All available global climate models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) are downscaled to provide likelihood and uncertainty estimates. By the end of century under RCP8.5, a distinctly new regional climate state emerges: average temperatures will almost certainly be outside the interannual variability range seen in the baseline. Except for the highest elevations and a narrow swath very near the coast, land locations will likely see 60–90 additional extremely hot days per year, effectively adding a new season of extreme heat. In mountainous areas, a majority of the many baseline days with freezing nighttime temperatures will most likely not occur. According to a similarity metric that measures daily temperature variability and the climate change signal, the RCP8.5 end-of-century climate will most likely be only about 50% similar to the baseline. For midcentury under RCP2.6 and RCP8.5 and end of century under RCP2.6, these same measures also indicate a detectable though less significant climatic shift. Therefore, while measures reducing global emissions would not prevent climate change at this regional scale in the coming decades, their impact would be dramatic by the end of the twenty-first century.

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

The Journal of Agricultural Science ◽

10.1017/s0021859621000101 ◽

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.

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

Current World Environment ◽

10.12944/cwe.11.2.40 ◽

2016 ◽

Vol 11 (2) ◽

pp. 670-678 ◽

Cited By ~ 1

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.

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