High-end climate change impact on European water availability and stress: exploring the presence of biases

Abstract. Climate models project a much more substantial warming than the 2 °C target making higher end scenarios increasingly plausible. Freshwater availability under such conditions is a key issue of concern. In this study, an ensemble of Euro-CORDEX projections under RCP8.5 is used to assess the mean and low hydrological states under +4 °C of global warming for the European region. Five major European catchments were analyzed in terms of future drought climatology and the impact of +2 vs. +4 °C global warming was investigated. The effect of bias correction of the climate model outputs and the observations used for this adjustment was also quantified. Projections indicate an intensification of the water cycle at higher levels of warming. Even for areas where the average state may not considerably be affected, low flows are expected to reduce leading to changes in the number of dry days and thus drought climatology. The identified increasing or decreasing runoff trends are substantially intensified when moving from the +2 to the +4 °C of global warming. Bias correction resulted in an improved representation of the historical hydrology. It is also found that the selection of the observational dataset for the application of the bias correction has an impact on the projected signal that could be of the same order of magnitude to the selection of the RCM.

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High-end climate change impact on European runoff and low flows – exploring the effects of forcing biases

Hydrology and Earth System Sciences ◽

10.5194/hess-20-1785-2016 ◽

2016 ◽

Vol 20 (5) ◽

pp. 1785-1808 ◽

Cited By ~ 29

Author(s):

Lamprini V. Papadimitriou ◽

Aristeidis G. Koutroulis ◽

Manolis G. Grillakis ◽

Ioannis K. Tsanis

Keyword(s):

Global Warming ◽

Bias Correction ◽

Climate Models ◽

Climate Model ◽

Global Climate ◽

Global Climate Model ◽

Data Set ◽

Low Flows ◽

The Impact ◽

Selection Of

Abstract. Climate models project a much more substantial warming than the 2 °C target under the more probable emission scenarios, making higher-end scenarios increasingly plausible. Freshwater availability under such conditions is a key issue of concern. In this study, an ensemble of Euro-CORDEX projections under RCP8.5 is used to assess the mean and low hydrological states under +4 °C of global warming for the European region. Five major European catchments were analysed in terms of future drought climatology and the impact of +2 °C versus +4 °C global warming was investigated. The effect of bias correction of the climate model outputs and the observations used for this adjustment was also quantified. Projections indicate an intensification of the water cycle at higher levels of warming. Even for areas where the average state may not considerably be affected, low flows are expected to reduce, leading to changes in the number of dry days and thus drought climatology. The identified increasing or decreasing runoff trends are substantially intensified when moving from the +2 to the +4° of global warming. Bias correction resulted in an improved representation of the historical hydrology. It is also found that the selection of the observational data set for the application of the bias correction has an impact on the projected signal that could be of the same order of magnitude to the selection of the Global Climate Model (GCM).

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Evaluating the Consistency between Statistically Downscaled and Global Dynamical Model Climate Change Projections

Journal of Climate ◽

10.1175/2008jcli2379.1 ◽

2008 ◽

Vol 21 (22) ◽

pp. 6052-6059 ◽

Cited By ~ 31

Author(s):

B. Timbal ◽

P. Hope ◽

S. Charles

Keyword(s):

Global Warming ◽

Statistical Downscaling ◽

Large Scale ◽

Climate Models ◽

Climate Model ◽

Specific Humidity ◽

Model Output ◽

Winter Rainfall ◽

Direct Model ◽

The Impact

Abstract The consistency between rainfall projections obtained from direct climate model output and statistical downscaling is evaluated. Results are averaged across an area large enough to overcome the difference in spatial scale between these two types of projections and thus make the comparison meaningful. Undertaking the comparison using a suite of state-of-the-art coupled climate models for two forcing scenarios presents a unique opportunity to test whether statistical linkages established between large-scale predictors and local rainfall under current climate remain valid in future climatic conditions. The study focuses on the southwest corner of Western Australia, a region that has experienced recent winter rainfall declines and for which climate models project, with great consistency, further winter rainfall reductions due to global warming. Results show that as a first approximation the magnitude of the modeled rainfall decline in this region is linearly related to the model global warming (a reduction of about 9% per degree), thus linking future rainfall declines to future emission paths. Two statistical downscaling techniques are used to investigate the influence of the choice of technique on projection consistency. In addition, one of the techniques was assessed using different large-scale forcings, to investigate the impact of large-scale predictor selection. Downscaled and direct model projections are consistent across the large number of models and two scenarios considered; that is, there is no tendency for either to be biased; and only a small hint that large rainfall declines are reduced in downscaled projections. Among the two techniques, a nonhomogeneous hidden Markov model provides greater consistency with climate models than an analog approach. Differences were due to the choice of the optimal combination of predictors. Thus statistically downscaled projections require careful choice of large-scale predictors in order to be consistent with physically based rainfall projections. In particular it was noted that a relative humidity moisture predictor, rather than specific humidity, was needed for downscaled projections to be consistent with direct model output projections.

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Assessing the impact of global warming on worldwide open field tomato cultivation through CSIRO-Mk3·0 global climate model

The Journal of Agricultural Science ◽

10.1017/s0021859616000654 ◽

2016 ◽

Vol 155 (3) ◽

pp. 407-420 ◽

Cited By ~ 7

Author(s):

R. S. SILVA ◽

L. KUMAR ◽

F. SHABANI ◽

M. C. PICANÇO

Keyword(s):

Climate Change ◽

Global Warming ◽

Open Field ◽

Climate Models ◽

Climate Model ◽

Global Climate ◽

Global Climate Model ◽

Current Model ◽

Global Climate Models ◽

The Impact

SUMMARYTomato (Solanum lycopersicum L.) is one of the most important vegetable crops globally and an important agricultural sector for generating employment. Open field cultivation of tomatoes exposes the crop to climatic conditions, whereas greenhouse production is protected. Hence, global warming will have a greater impact on open field cultivation of tomatoes rather than the controlled greenhouse environment. Although the scale of potential impacts is uncertain, there are techniques that can be implemented to predict these impacts. Global climate models (GCMs) are useful tools for the analysis of possible impacts on a species. The current study aims to determine the impacts of climate change and the major factors of abiotic stress that limit the open field cultivation of tomatoes in both the present and future, based on predicted global climate change using CLIMatic indEX and the A2 emissions scenario, together with the GCM Commonwealth Scientific and Industrial Research Organisation (CSIRO)-Mk3·0 (CS), for the years 2050 and 2100. The results indicate that large areas that currently have an optimum climate will become climatically marginal or unsuitable for open field cultivation of tomatoes due to progressively increasing heat and dry stress in the future. Conversely, large areas now marginal and unsuitable for open field cultivation of tomatoes will become suitable or optimal due to a decrease in cold stress. The current model may be useful for plant geneticists and horticulturalists who could develop new regional stress-resilient tomato cultivars based on needs related to these modelling projections.

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Selection of Bias Correction Methods to Assess the Impact of Climate Change on Flood Frequency Curves

Water ◽

10.3390/w11112266 ◽

2019 ◽

Vol 11 (11) ◽

pp. 2266 ◽

Cited By ~ 4

Author(s):

Enrique Soriano ◽

Luis Mediero ◽

Carlos Garijo

Keyword(s):

Climate Change ◽

Bias Correction ◽

Climate Models ◽

Climate Model ◽

Control Period ◽

Flood Frequency ◽

Climate Projections ◽

The Future ◽

Flood Quantiles ◽

The Impact

Climate projections provided by EURO-CORDEX predict changes in annual maximum series of daily rainfall in the future in some areas of Spain because of climate change. Precipitation and temperature projections supplied by climate models do not usually fit exactly the statistical properties of the observed time series in the control period. Bias correction methods are used to reduce such errors. This paper seeks to find the most adequate bias correction techniques for temperature and precipitation projections that minimizes the errors between observations and climate model simulations in the control period. Errors in flood quantiles are considered to identify the best bias correction techniques, as flood quantiles are used for hydraulic infrastructure design and safety assessment. In addition, this study aims to understand how the expected changes in precipitation extremes and temperature will affect the catchment response in flood events in the future. Hydrological modelling is required to characterize rainfall-runoff processes adequately in a changing climate, in order to estimate flood changes expected in the future. Four catchments located in the central-western part of Spain have been selected as case studies. The HBV hydrological model has been calibrated in the four catchments by using the observed precipitation, temperature and streamflow data available on a daily scale. Rainfall has been identified as the most significant input to the model, in terms of its influence on flood response. The quantile mapping polynomial correction has been found to be the best bias correction method for precipitation. A general reduction in flood quantiles is expected in the future, smoothing the increases identified in precipitation quantiles by the reduction of soil moisture content in catchments, due to the expected increase in temperature and decrease in mean annual precipitations.

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Evaluation and selection of CORDEX-SA datasets and bias correction methods for a hydrological impact study in a humid tropical river basin, Kerala

Journal of Water and Climate Change ◽

10.2166/wcc.2021.139 ◽

2021 ◽

Author(s):

M. S. Saranya ◽

V. Nair Vinish

Keyword(s):

River Basin ◽

Bias Correction ◽

General Circulation ◽

Climate Models ◽

Climate Model ◽

Circulation Model ◽

Climate Variables ◽

Tropical River ◽

Hydrological Impact ◽

Selection Of

Abstract It is well recognised that the performance of climate model simulations and bias correction methods is region specific, and, therefore, careful validation should always be performed. This study evaluates the performance of five general circulation model–regional climate model (GCM–RCM) combinations selected from CORDEX–SA datasets over a humid tropical river basin in Kerala, India, for climate variables such as precipitation, maximum and minimum temperatures. This involves ranking of the selected climate models based on an EDAS (Evaluation Based on Distance from Average Solution) method and the selection of an appropriate bias correction method for the selected three climate variables. A range of indices are used to evaluate the performance of the bias-corrected climate models to simulate observed climate data. Finally, the hydrological impact of the bias-corrected ranked models is assessed by simulating streamflow over the river basin using individual models and different combinations of models based on rank. According to the findings, hydrological simulation using an average of all GCM–RCM pairs provides the best model output in simulating streamflow, with an NSE value of 0.72. The results confirm the importance of a multimodel ensemble for improving the reliability and minimising the uncertainty of climate predictions for impact studies.

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Modeling the Response of Marine Boundary Layer Clouds to Global Warming: The Impact of Subgrid-Scale Precipitation Formation

Journal of Climate ◽

10.1175/jcli-d-11-00623.1 ◽

2012 ◽

Vol 25 (19) ◽

pp. 6610-6626 ◽

Cited By ~ 4

Author(s):

Axel Lauer ◽

Ralf Bennartz ◽

Kevin Hamilton ◽

Yuqing Wang

Keyword(s):

Global Warming ◽

Climate Models ◽

Climate Model ◽

Integral Constraint ◽

Subgrid Scale ◽

Model Tuning ◽

Formation Efficiency ◽

The Impact ◽

Constraint Method ◽

Precipitation Formation

Abstract An important parameter often adjusted to achieve agreement between simulated and observed radiative fluxes in climate models is the rain formation efficiency. This adjustment has been justified as accounting for the effects of subgrid-scale variability in cloud properties, but this tuning approach is rather arbitrary. This study examines results from a regional climate model with precipitation formation schemes that have been conventionally tuned, and it compares them with simulations employing a recently developed scheme that uses satellite observations to explicitly account for the subgrid-scale variability of clouds (“integral constraint method”). Simulations with the International Pacific Research Center’s Regional Atmospheric Model (iRAM) show that the integral constraint method is capable of simulating cloud fields over the eastern Pacific that are in good agreement with observations, without requiring model tuning. A series of global warming simulations for late twenty-first-century conditions is performed to investigate the impact of the treatment of the precipitation formation efficiency on modeled cloud–climate feedbacks. The results with the integral constraint method show that the simulated cloud feedbacks have similar patterns at all the model resolutions considered (grid spacings of 50, 100, and 200 km), but there are some quantitative differences (with smaller feedbacks at finer resolution). The cloud responses to global warming in simulations with a conventionally tuned autoconversion scheme and with the integral constraint method were found to be quite consistent, although differences in individual regions of ~10%–30% are evident. No conclusions can be drawn from this study on the validity of model tuning for thick clouds and mixed phase or ice clouds, however.

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Impacts of bias nonstationarity of climate model outputs on hydrological simulations

Hydrology Research ◽

10.2166/nh.2020.254 ◽

2020 ◽

Vol 51 (5) ◽

pp. 925-941

Author(s):

Yu Hui ◽

Yuni Xu ◽

Jie Chen ◽

Chong-Yu Xu ◽

Hua Chen

Keyword(s):

Climate Change ◽

Bias Correction ◽

Climate Models ◽

Climate Model ◽

Time Windows ◽

Natural Climate Variability ◽

Change Impact ◽

Natural Climate ◽

Precipitation And Temperature

Abstract Bias correction methods are based on the assumption of bias stationarity of climate model outputs. However, this assumption may not be valid, because of the natural climate variability. This study investigates the impacts of bias nonstationarity of climate models simulated precipitation and temperature on hydrological climate change impact studies. The bias nonstationarity is determined as the range of difference in bias over multiple historical periods with no anthropogenic climate change for four different time windows. The role of bias nonstationarity in future climate change is assessed using the signal-to-noise ratio as a criterion. The results show that biases of climate models simulated monthly and annual precipitation and temperature vary with time, especially for short time windows. The bias nonstationarity of precipitation plays a great role in future precipitation change, while the role of temperature bias is not important. The bias nonstationarity of climate model outputs is amplified when driving a hydrological model for hydrological simulations. The increase in the length of time window can mitigate the impacts of bias nonstationarity for streamflow projections. Thus, a long time period is suggested to be used to calibrate a bias correction method for hydrological climate change impact studies to reduce the influence of natural climate variability.

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Exploring the performance of bias correction applied outside the calibration period’s climate regime

10.5194/egusphere-egu2020-13211 ◽

2020 ◽

Author(s):

Katharina Klehmet ◽

Peter Berg ◽

Pascual Herrera ◽

David Leidinger ◽

Anthony Lemoine ◽

...

Keyword(s):

Bias Correction ◽

Climate Models ◽

Climate Model ◽

Joint Probability ◽

Correction Method ◽

Calibration Data ◽

Climate Regime ◽

Split Sample ◽

Set Up ◽

The Impact

<p>It is common practice to apply some form of bias correction to climate models before use in impact modelling, such as hydrology. The standard method is to evaluate the correction method based on a cross validation procedure with two or more sub-periods. This allows the method to be assessed on data not previously seen in the calibration step. However, with standard split-sample setups, the data is most likely in a similar climate regime as the calibration data. In effect, the method is evaluated in the same climate regime as it is calibrated, and informs little about the performance outside the current climate regime.</p><p>To address this issue, a discrete split sample test (DSST) was set up so that as diverse climate regimes as possible were sampled. The simplest climate analogue would be to perform the DSST on the coldest years and evaluate on the warmest, to mimic a changing temperature. Here, the tests are extended to more exotic indicators, such as snow pack, the joint probability of wet and cold seasons, the number of hot days in a year, the convective activity during summer; all related to a specific case study issue. Six different bias correction methods of both standard quantile mapping and other approaches to scale the reference time series are included. The methods are applied in a pseudo-reality framework to six climate model projections from Euro-CORDEX 12.5 km simulations. The analysis is focused on comparing the DSST performance with the impact on the climate change signals, and to the reliability of each method when applied to different climate regimes.</p>

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Regional Projections of Future Seasonal and Annual Changes in Rainfall and Temperature over Australia Based on Skill-Selected AR4 Models

Earth Interactions ◽

10.1175/2008ei260.1 ◽

2008 ◽

Vol 12 (12) ◽

pp. 1-50 ◽

Cited By ~ 51

Author(s):

A. J. Pitman ◽

S. E. Perkins

Keyword(s):

Climate Models ◽

Climate Model ◽

Temperature Response ◽

Extreme Rainfall ◽

Maximum Temperature ◽

Intergovernmental Panel ◽

Daily Data ◽

Projected Change ◽

The Mean ◽

The Impact

Abstract Daily data from climate models submitted to the Fourth Assessment of the Intergovernmental Panel on Climate Change are compared with daily data from observations over Australia by measuring the overlap of the probability density functions (PDFs). The capacity of these models to simulate maximum temperature, minimum temperature, and precipitation is assessed. The resulting skill score is then used to exclude models with relatively poor skill region by region over Australia. The remaining sample of coupled climate models is then used to determine the seasonal changes in these three variables under a high- (A2) and low- (B1) emission scenario for 2050 and 2100. The authors demonstrate that some projected phenomena, such as the projected drying over southwest Western Australia, are robust and not caused by the inclusion of some weak models in earlier assessments. Some other results, such as the projected change in the monsoon, are more consistent among the good climate models. Consistent with earlier work, a consistent pattern of mean warming is identified in the projections. The amount of warming in the 99.7th percentile is not dramatically higher than the warming in the mean. However, while the mean warming is generally least in the south, the amount of warming in the 99.7th percentile is substantially higher along the southern coast of Australia. This is due to a coupling of the temperature response with reduced rainfall, which causes drying and allows extreme maximum temperatures to increase dramatically. The authors show that, in general, the amount of rainfall is projected to change relatively little, but the frequency of rainfall decreases and the intensity of rainfall at the upper tail of the distribution increases. However, the scale of the increase in extreme rainfall is not large on the time scales analyzed here. The range in projected temperature changes among those climate models with skill in simulating the observations is at least twice as large for the 99.7th/0.3rd percentiles as for the mean. For rainfall, the range among the good models is of order 10 times greater in the 99.7th percentile than in the mean. Since the impact of changes in extremes is increasingly recognized as societally important, this result strongly limits the use of climate model data to explore sectors that are vulnerable to extremes. This suggests an evaluation strategy that focuses on model capacity to simulate whole PDFs since capacity to simulate the mean is a necessary but insufficient criterion for determining a model’s value for future projection.

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Biases in CloudSat Falling Snow Estimates Resulting from Daylight-Only Operations

Remote Sensing ◽

10.3390/rs13112041 ◽

2021 ◽

Vol 13 (11) ◽

pp. 2041

Author(s):

Lisa Milani ◽

Norman B. Wood

Keyword(s):

Water Cycle ◽

Mean Values ◽

The Mean ◽

Satellite Missions ◽

The Impact ◽

Global Mean

Falling snow is a key component of the Earth’s water cycle, and space-based observations provide the best current capability to evaluate it globally. The Cloud Profiling Radar (CPR) on board CloudSat is sensitive to snowfall, and other satellite missions and climatological models have used snowfall properties measured by it for evaluating and comparing against their snowfall products. Since a battery anomaly in 2011, the CPR has operated in a Daylight-Only Operations (DO-Op) mode, in which it makes measurements primarily during only the daylit portion of its orbit. This work provides estimates of biases inherent in global snowfall amounts derived from CPR measurements due to this shift to DO-Op mode. We use CloudSat’s snowfall measurements during its Full Operations (Full-Op) period prior to the battery anomaly to evaluate the impact of the DO-Op mode sampling. For multi-year global mean values, the snowfall fraction during DO-Op changes by −10.16% and the mean snowfall rate changes by −8.21% compared with Full-Op. These changes are driven by the changes in sampling in DO-Op and are very little influenced by changes in meteorology between the Full-Op and DO-Op periods. The results highlight the need to sample consistently with the CloudSat observations or to adjust snowfall estimates derived from CloudSat when using DO-Op data to evaluate other precipitation products.

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