Future precipitation changes over the eastern Adriatic and Dinaric Alps areas in the latest EURO-CORDEX ensemble

The topographically complex coastal-mountainous region of the eastern Adriatic and Dinaric Alps is one of the rainiest areas in the Mediterranean and particularly vulnerable to climate change. The aim is to estimate the future climate change of precipitation over this region over which research on this subject is still limited. We use the climate projections from the latest EURO-CORDEX ensemble at 0.11&#176; resolution. The ensemble is comprised of 14 regional climate models (RCMs) driven by eight CMIP5 global climate models (GCMs), a total of 68 members. The climate change signal is examined for the far future period (2071-2100) with respect to the historical period (1971-2000) for one greenhouse gases concentration scenario, particularly for RCP8.5. Total precipitation shows a considerable reduction in summer months, while in winter it is projected to increase in the northern part of the region and to decrease in southern parts, displaying the known south-north gradients. Accordingly, the number of rainy days is projected to decrease by the end of the century, especially during summer over the entire region and in winter over the southern parts. However, the precipitation intensity increase can be expected by the end of the century, especially during the winter months, while in the summer there is no clear consensus between different models. Also, an increase in extreme precipitation is projected during the winter months, while during summer months a similar south-north gradient is shown as for total precipitation. A more detailed analysis for multiple future periods and greenhouse gases concentration scenarios, with an emphasis on extreme precipitation, is planned.

Download Full-text

Projected local rain events due to climate change and the impacts on waterborne diseases in Vancouver, British Columbia, Canada

Environmental Health ◽

10.1186/s12940-019-0550-y ◽

2019 ◽

Vol 18 (1) ◽

Cited By ~ 2

Author(s):

Bimal K. Chhetri ◽

Eleni Galanis ◽

Stephen Sobie ◽

Jordan Brubacher ◽

Robert Balshaw ◽

...

Keyword(s):

Climate Change ◽

Extreme Precipitation ◽

Climate Models ◽

Extreme Weather Events ◽

Historical Period ◽

Climate Projections ◽

Observation Data ◽

Waterborne Diseases ◽

Extreme Precipitation Events ◽

Extreme Rain

Abstract Background Climate change is increasing the number and intensity of extreme weather events in many parts of the world. Precipitation extremes have been linked to both outbreaks and sporadic cases of waterborne illness. We have previously shown a link between heavy rain and turbidity to population-level risk of sporadic cryptosporidiosis and giardiasis in a major Canadian urban population. The risk increased with 30 or more dry days in the 60 days preceding the week of extreme rain. The goal of this study was to investigate the change in cryptosporidiosis and giardiasis risk due to climate change, primarily change in extreme precipitation. Methods Cases of cryptosporidiosis and giardiasis were extracted from a reportable disease system (1997–2009). We used distributed lag non-linear Poisson regression models and projections of the exposure-outcome relationship to estimate future illness (2020–2099). The climate projections are derived from twelve statistically downscaled regional climate models. Relative Concentration Pathway 8.5 was used to project precipitation derived from daily gridded weather observation data (~ 6 × 10 km resolution) covering the central of three adjacent watersheds serving metropolitan Vancouver for the 2020s, 2040s, 2060s and 2080s. Results Precipitation is predicted to steadily increase in these watersheds during the wet season (Oct. -Mar.) and decrease in other parts of the year up through the 2080s. More weeks with extreme rain (>90th percentile) are expected. These weeks are predicted to increase the annual rates of cryptosporidiosis and giardiasis by approximately 16% by the 2080s corresponding to an increase of 55–136 additional cases per year depending upon the climate model used. The predicted increase in the number of waterborne illness cases are during the wet months. The range in future projections compared to historical monthly case counts typically differed by 10–20% across climate models but the direction of change was consistent for all models. Discussion If new water filtration measures had not been implemented in our study area in 2010–2015, the risk of cryptosporidiosis and giardiasis would have been expected to increase with climate change, particularly precipitation changes. In addition to the predicted increase in the frequency and intensity of extreme precipitation events, the frequency and length of wet and dry spells could also affect the risk of waterborne diseases as we observed in the historical period. These findings add to the growing evidence regarding the need to prepare water systems to manage and become resilient to climate change-related health risks.

Download Full-text

Evaluation of uncertainties in mean and extreme precipitation under climate change for northwestern Mediterranean watersheds from high-resolution Med and Euro-CORDEX ensembles

Hydrology and Earth System Sciences ◽

10.5194/hess-22-673-2018 ◽

2018 ◽

Vol 22 (1) ◽

pp. 673-687 ◽

Cited By ~ 10

Author(s):

Antoine Colmet-Daage ◽

Emilia Sanchez-Gomez ◽

Sophie Ricci ◽

Cécile Llovel ◽

Valérie Borrell Estupina ◽

...

Keyword(s):

Climate Change ◽

High Resolution ◽

Extreme Precipitation ◽

Climate Models ◽

Regional Climate ◽

Regional Climate Models ◽

Historical Period ◽

Mountainous Regions ◽

Extreme Precipitation Events ◽

Precipitation Events

Abstract. The climate change impact on mean and extreme precipitation events in the northern Mediterranean region is assessed using high-resolution EuroCORDEX and MedCORDEX simulations. The focus is made on three regions, Lez and Aude located in France, and Muga located in northeastern Spain, and eight pairs of global and regional climate models are analyzed with respect to the SAFRAN product. First the model skills are evaluated in terms of bias for the precipitation annual cycle over historical period. Then future changes in extreme precipitation, under two emission scenarios, are estimated through the computation of past/future change coefficients of quantile-ranked model precipitation outputs. Over the 1981–2010 period, the cumulative precipitation is overestimated for most models over the mountainous regions and underestimated over the coastal regions in autumn and higher-order quantile. The ensemble mean and the spread for future period remain unchanged under RCP4.5 scenario and decrease under RCP8.5 scenario. Extreme precipitation events are intensified over the three catchments with a smaller ensemble spread under RCP8.5 revealing more evident changes, especially in the later part of the 21st century.

Download Full-text

Projecting malaria hazard from climate change in eastern Africa using large ensembles to estimate uncertainty

Geospatial health ◽

10.4081/gh.2016.393 ◽

2016 ◽

Vol 11 (1s) ◽

Cited By ~ 8

Author(s):

Joseph Leedale ◽

Adrian M. Tompkins ◽

Cyril Caminade ◽

Anne E. Jones ◽

Grigory Nikulin ◽

...

Keyword(s):

Climate Change ◽

Malaria Transmission ◽

Climate Models ◽

Climate Model ◽

Eastern Africa ◽

Future Climate Change ◽

Climate Projections ◽

Model Ensemble ◽

East African Community ◽

Recent Climate

The effect of climate change on the spatiotemporal dynamics of malaria transmission is studied using an unprecedented ensemble of climate projections, employing three diverse bias correction and downscaling techniques, in order to partially account for uncertainty in climate- driven malaria projections. These large climate ensembles drive two dynamical and spatially explicit epidemiological malaria models to provide future hazard projections for the focus region of eastern Africa. While the two malaria models produce very distinct transmission patterns for the recent climate, their response to future climate change is similar in terms of sign and spatial distribution, with malaria transmission moving to higher altitudes in the East African Community (EAC) region, while transmission reduces in lowland, marginal transmission zones such as South Sudan. The climate model ensemble generally projects warmer and wetter conditions over EAC. The simulated malaria response appears to be driven by temperature rather than precipitation effects. This reduces the uncertainty due to the climate models, as precipitation trends in tropical regions are very diverse, projecting both drier and wetter conditions with the current state-of-the-art climate model ensemble. The magnitude of the projected changes differed considerably between the two dynamical malaria models, with one much more sensitive to climate change, highlighting that uncertainty in the malaria projections is also associated with the disease modelling approach.

Download Full-text

Evaluating quantile-based bias adjustment methods for climate change scenarios

10.5194/hess-2021-498 ◽

2021 ◽

Author(s):

Fabian Lehner ◽

Imran Nadeem ◽

Herbert Formayer

Keyword(s):

Climate Change ◽

Climate Models ◽

Climate Model ◽

Meteorological Data ◽

Climate Change Signal ◽

Historical Period ◽

Climate Change Scenarios ◽

Statistical Bias ◽

Bias Adjustment ◽

Direct Model

Abstract. Daily meteorological data such as temperature or precipitation from climate models is needed for many climate impact studies, e.g. in hydrology or agriculture but direct model output can contain large systematic errors. Thus, statistical bias adjustment is applied to correct climate model outputs. Here we review existing statistical bias adjustment methods and their shortcomings, and present a method which we call EQA (Empirical Quantile Adjustment), a development of the methods EDCDFm and PresRAT. We then test it in comparison to two existing methods using real and artificially created daily temperature and precipitation data for Austria. We compare the performance of the three methods in terms of the following demands: (1): The model data should match the climatological means of the observational data in the historical period. (2): The long-term climatological trends of means (climate change signal), either defined as difference or as ratio, should not be altered during bias adjustment, and (3): Even models with too few wet days (precipitation above 0.1 mm) should be corrected accurately, so that the wet day frequency is conserved. EQA fulfills (1) almost exactly and (2) at least for temperature. For precipitation, an additional correction included in EQA assures that the climate change signal is conserved, and for (3), we apply another additional algorithm to add precipitation days.

Download Full-text

Reduced global warming from CMIP6 projections when weighting models by performance and independence

Earth System Dynamics ◽

10.5194/esd-11-995-2020 ◽

2020 ◽

Vol 11 (4) ◽

pp. 995-1012

Author(s):

Lukas Brunner ◽

Angeline G. Pendergrass ◽

Flavio Lehner ◽

Anna L. Merrifield ◽

Ruth Lorenz ◽

...

Keyword(s):

Climate Change ◽

Retrospective Analysis ◽

Climate Models ◽

Cmip5 Models ◽

Future Climate Change ◽

Historical Period ◽

Climate Change Scenarios ◽

Uncertainty Range ◽

Future Warming ◽

The Mean

Abstract. The sixth Coupled Model Intercomparison Project (CMIP6) constitutes the latest update on expected future climate change based on a new generation of climate models. To extract reliable estimates of future warming and related uncertainties from these models, the spread in their projections is often translated into probabilistic estimates such as the mean and likely range. Here, we use a model weighting approach, which accounts for the models' historical performance based on several diagnostics as well as model interdependence within the CMIP6 ensemble, to calculate constrained distributions of global mean temperature change. We investigate the skill of our approach in a perfect model test, where we use previous-generation CMIP5 models as pseudo-observations in the historical period. The performance of the distribution weighted in the abovementioned manner with respect to matching the pseudo-observations in the future is then evaluated, and we find a mean increase in skill of about 17 % compared with the unweighted distribution. In addition, we show that our independence metric correctly clusters models known to be similar based on a CMIP6 “family tree”, which enables the application of a weighting based on the degree of inter-model dependence. We then apply the weighting approach, based on two observational estimates (the fifth generation of the European Centre for Medium-Range Weather Forecasts Retrospective Analysis – ERA5, and the Modern-Era Retrospective analysis for Research and Applications, version 2 – MERRA-2), to constrain CMIP6 projections under weak (SSP1-2.6) and strong (SSP5-8.5) climate change scenarios (SSP refers to the Shared Socioeconomic Pathways). Our results show a reduction in the projected mean warming for both scenarios because some CMIP6 models with high future warming receive systematically lower performance weights. The mean of end-of-century warming (2081–2100 relative to 1995–2014) for SSP5-8.5 with weighting is 3.7 ∘C, compared with 4.1 ∘C without weighting; the likely (66%) uncertainty range is 3.1 to 4.6 ∘C, which equates to a 13 % decrease in spread. For SSP1-2.6, the weighted end-of-century warming is 1 ∘C (0.7 to 1.4 ∘C), which results in a reduction of −0.1 ∘C in the mean and −24 % in the likely range compared with the unweighted case.

Download Full-text

Evaluating the effects of climate change for groundwater quantitative status in Denmark

10.5194/egusphere-egu2020-19101 ◽

2020 ◽

Author(s):

Annesofie Jakosben ◽

Hans Jørgen Henriksen ◽

Ernesto Pasten-Zapata ◽

Torben Sonnenborg ◽

Lars Troldborg

Keyword(s):

Climate Change ◽

Surface Water ◽

Water Balance ◽

Overland Flow ◽

Future Climate Change ◽

Historical Period ◽

Climate Projections ◽

Monitoring Networks ◽

Groundwater Levels ◽

Ecological Flow

By use of transient and distributed groundwater-surface water flow models, simulated time series of stream discharge and groundwater level for monitoring networks, groundwater bodies and river reaches have been analysed for a historical period and four different future scenarios toward 2100 in two large-scale catchments in Denmark. The purpose of the climate scenarios has been to qualify the existing knowledge on how future climate change most likely will impact hydrology, groundwater status and Ecological Quality Elements (EQR- Ecological flow in rivers). Another purpose has been to identify whether foreseen climate changes will be detected by the surface water and groundwater monitoring networks, and to which degree the River Basin Management Plan measures for supporting the goal of good quantitative status are robust to the projected changes in water balance and ecological flow. The developed hydrological models were run with climate inputs based on selected RCP4.5 and RCP8.5 climate model runs (RCP8.5 wet, median, dry and RCP4.5 median). Changes in groundwater quantitative status and ecological flow metrics were calculated based on 30-year model runs driven by RCP8.5 for 2071-2100 (RCP4.5 for 2041-70) and compared to 1981-2010.Overall the four scenarios results in very significant water balance changes with increased precipitation: 3% to 27%, evapotranspiration: 6% to 17%, groundwater recharge: 0% to 49%, drainage flow: 0% to 71%, baseflow: 0% to 31% and overland flow: 16% to 281%. For one catchment an increase in abstraction of 23% to 171% due to an increase in irrigation demand by 36% to 113% is foreseen. The results have wide implications for groundwater flooding risks, quantitative status and ecological flow metrics. Most sensitive is changes in ecological flow conditions in rivers for fish, showing a relative high probability for decreased state for 10-20% of the reaches for the RCP8.5 wet and dry scenarios due to more extreme hydrological regimes toward 2071-2100. Maximum monthly runoff is increased for winter months by 100% for RCP8.5 wet and median scenarios and around 10% for RCP8.5 dry scenario. Annual maximum daily flows is simulated to increase by up to a factor of five, and late summer low flows decreased.Impacts on groundwater levels and water balances of groundwater bodies will be significant, with increased seasonal fluctuations and also increased maximum and decreased minimum groundwater levels for 30 year periods for 2071-2100 compared to 1981-2010.More rain, both when we look back on historical data and when we look forward with latest climate projections will result in more frequent flooding from groundwater and streams in the future. At the same time, the temperature and thus evapotranspiration rises. This means that in the long term we will have increased challenges with drought and increased irrigation demands on sandy soils while evapotranspiration will also increase on the clayey soils. This will result in greater fluctuation in the flow and groundwater levels between winters and summers, and between wet and dry years, challenging sustainable groundwater abstraction and maintaining good quantitative status of groundwater bodies.

Download Full-text

Historically-based run-time bias corrections substantially improve model projections of 100 years of future climate change

Communications Earth & Environment ◽

10.1038/s43247-020-00035-0 ◽

2020 ◽

Vol 1 (1) ◽

Author(s):

Gerhard Krinner ◽

Viatcheslav Kharin ◽

Romain Roehrig ◽

John Scinocca ◽

Francis Codron

Keyword(s):

Climate Change ◽

Climate Models ◽

Coupled Model ◽

Atmospheric Model ◽

Climate Impact ◽

Future Climate ◽

Future Climate Change ◽

Historical Period ◽

Underlying Assumption ◽

Bias Corrections

Abstract Climate models and/or their output are usually bias-corrected for climate impact studies. The underlying assumption of these corrections is that climate biases are essentially stationary between historical and future climate states. Under very strong climate change, the validity of this assumption is uncertain, so the practical benefit of bias corrections remains an open question. Here, this issue is addressed in the context of bias correcting the climate models themselves. Employing the ARPEGE, LMDZ and CanAM4 atmospheric models, we undertook experiments in which one centre’s atmospheric model takes another centre’s coupled model as observations during the historical period, to define the bias correction, and as the reference under future projections of strong climate change, to evaluate its impact. This allows testing of the stationarity assumption directly from the historical through future periods for three different models. These experiments provide evidence for the validity of the new bias-corrected model approach. In particular, temperature, wind and pressure biases are reduced by 40–60% and, with few exceptions, more than 50% of the improvement obtained over the historical period is on average preserved after 100 years of strong climate change. Below 3 °C global average surface temperature increase, these corrections globally retain 80% of their benefit.

Download Full-text

FAIR v1.3: a simple emissions-based impulse response and carbon cycle model

Geoscientific Model Development ◽

10.5194/gmd-11-2273-2018 ◽

2018 ◽

Vol 11 (6) ◽

pp. 2273-2297 ◽

Cited By ~ 37

Author(s):

Christopher J. Smith ◽

Piers M. Forster ◽

Myles Allen ◽

Nicholas Leach ◽

Richard J. Millar ◽

...

Keyword(s):

Climate Change ◽

Greenhouse Gases ◽

Radiative Forcing ◽

Climate Models ◽

Climate Model ◽

Future Climate Change ◽

Climate Response ◽

Transient Climate Response ◽

Carbon Cycle Model ◽

Future Projections

Abstract. Simple climate models can be valuable if they are able to replicate aspects of complex fully coupled earth system models. Larger ensembles can be produced, enabling a probabilistic view of future climate change. A simple emissions-based climate model, FAIR, is presented, which calculates atmospheric concentrations of greenhouse gases and effective radiative forcing (ERF) from greenhouse gases, aerosols, ozone and other agents. Model runs are constrained to observed temperature change from 1880 to 2016 and produce a range of future projections under the Representative Concentration Pathway (RCP) scenarios. The constrained estimates of equilibrium climate sensitivity (ECS), transient climate response (TCR) and transient climate response to cumulative CO2 emissions (TCRE) are 2.86 (2.01 to 4.22) K, 1.53 (1.05 to 2.41) K and 1.40 (0.96 to 2.23) K (1000 GtC)−1 (median and 5–95 % credible intervals). These are in good agreement with the likely Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) range, noting that AR5 estimates were derived from a combination of climate models, observations and expert judgement. The ranges of future projections of temperature and ranges of estimates of ECS, TCR and TCRE are somewhat sensitive to the prior distributions of ECS∕TCR parameters but less sensitive to the ERF from a doubling of CO2 or the observational temperature dataset used to constrain the ensemble. Taking these sensitivities into account, there is no evidence to suggest that the median and credible range of observationally constrained TCR or ECS differ from climate model-derived estimates. The range of temperature projections under RCP8.5 for 2081–2100 in the constrained FAIR model ensemble is lower than the emissions-based estimate reported in AR5 by half a degree, owing to differences in forcing assumptions and ECS∕TCR distributions.

Download Full-text

Bias Correction of RCM Precipitation by TIN-Copula Method: A Case Study for Historical and Future Simulations in Cyprus

Climate ◽

10.3390/cli8070085 ◽

2020 ◽

Vol 8 (7) ◽

pp. 85

Author(s):

Georgia Lazoglou ◽

George Zittis ◽

Christina Anagnostopoulou ◽

Panos Hadjinicolaou ◽

Jos Lelieveld

Keyword(s):

Bias Correction ◽

Extreme Precipitation ◽

Numerical Models ◽

Climate Simulation ◽

Climate Change Signal ◽

Historical Period ◽

Total Precipitation ◽

Climate Conditions ◽

Recent Climate ◽

Copula Method

Numerical models are being used for the simulation of recent climate conditions as well as future projections. Due to the complexity of the Earth’s climate system and processes occurring at sub-grid scales, model results often diverge from the observed values. Different methods have been developed to minimize such biases. In the present study, the recently introduced “triangular irregular networks (TIN)-Copula” method was used for the bias correction of modelled monthly total and extreme precipitation in Cyprus. The method was applied to a 15-year historical period and two future periods of the same duration. Precipitation time-series were derived from a 12-km resolution EURO-CORDEX regional climate simulation. The results show that the TIN-Copula method significantly reduces the positive biases between the model results and observations during the historical period of 1986–2000, for both total and extreme precipitation (>80%). However, the level of improvement differs temporally and spatially. For future periods, the model tends to project significantly higher total precipitation rates prior to bias correction, while for extremes the differences are smaller. The adjustments slightly affect the overall climate change signal, which tends to be enhanced after bias correction, especially for total precipitation and for the autumn period.

Download Full-text

Impact of climate change on water resources in a tropical West African catchment using an ensemble of climate simulations

10.5194/hess-2016-387 ◽

2016 ◽

Cited By ~ 1

Author(s):

Yacouba Yira ◽

Bernd Diekkrüger ◽

Gero Steup ◽

Aymar Y. Bossa

Keyword(s):

Climate Change ◽

Water Resources ◽

Bias Correction ◽

Climate Models ◽

Regional Climate ◽

West African ◽

Climate Change Signal ◽

Climate Projections ◽

Impact Of Climate Change ◽

High Uncertainty

Abstract. This study evaluates climate change impacts on water resources using an ensemble of six Regional Climate Models (RCMs)-Global Climate Models (GCMs) in the Dano catchment (Burkina Faso). The applied climate datasets were performed in the framework of the COordinated Regional climate Downscaling Experiment (CORDEX-Africa) project. After evaluation of the historical runs of the climate models ensemble, a statistical bias correction (Empirical Quantile Mapping) was applied to daily precipitation. Temperature and bias corrected precipitation data from the ensemble of RCMs-GCMs was then used as input for the Water flow and balance Simulation Model (WaSiM) to simulate water balance components. The mean hydrological and climate variables for two periods (1971–2000 and 2021–2050) were compared to assess the potential impact of climate change on water resources up to the middle of the twenty-first century under two greenhouse gas concentration scenarios, the Representative Concentration Pathways (RCPs) 4.5 and 8.5. The results indicate: (i) a clear signal of temperature increase of about 0.1 to 2.6 °C for all members of the RCMs-GCMs ensemble; (ii) high uncertainty about how the catchment precipitation will evolve over the period 2021–2050; (iii) individual climate models results lead to opposite discharge change signals; (iv) the RCMs-GCMs ensemble average suggests a +7 % increase in annual discharge under RCP4.5 and a −2 % decrease under RCP8.5; (v) the applied bias correction method only affected the magnitude of climate change signal. Therefore, potential increase and decrease of future discharge has to be considered in climate change adaptation strategies in the catchment. The results further underline on the one hand the need for a larger ensemble of projections to properly estimate the impacts of climate change on water resources in the catchment and on the other hand the high uncertainty associated with climate projections for the West African region. An ecohydrological analysis provides further insight into the behavior of the catchment.

Download Full-text