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

Extreme Events Representation in CMCC-CM2 High and Very-High Resolution General Circulation Models

10.5194/gmd-2021-294 ◽

2021 ◽

Author(s):

Enrico Scoccimarro ◽

Daniele Peano ◽

Silvio Gualdi ◽

Alessio Bellucci ◽

Tomas Lovato ◽

...

Keyword(s):

High Resolution ◽

Extreme Events ◽

General Circulation ◽

Climate Models ◽

General Circulation Models ◽

Horizontal Resolution ◽

Historical Period ◽

Climate Modelling ◽

Temperature And Precipitation ◽

Very High

Abstract. The recent advancements in climate modelling partially build on the improvement of horizontal resolution in different components of the simulating system. A higher resolution is expected to provide a better representation of the climate variability, and in this work we are particularly interested in the potential improvements in representing extreme events of high temperature and precipitation. The two versions of the CMCC-CM2 model used here, adopt the highest horizontal resolutions available within the last family of the global coupled climate models de¬veloped at CMCC to participate in the CMIP6 effort. The main aim of this study is to document the ability of the CMCC-CM2 models in representing the spatial distribution of extreme events of temperature and precipitation, under the historical period, comparing model results to observations (ERA5 Reanalysis and CHIRPS observations). For a more detailed evaluation we investigate both 6 hourly and daily time series for the definition of the extreme conditions. In terms of mean climate, the two models are able to realistically reproduce the main patterns of temperature and precipitation. The very-high resolution version (¼ degree horizontal resolution) of the atmospheric model provides better results than the high resolution one (one degree), not only in terms of means but also in terms of extreme events of temperature defined at daily and 6-hourly frequency. This is also the case of average precipitation. On the other hand the extreme precipitation is not improved by the adoption of a higher horizontal resolution.

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What is the added value of using downscaled CMIP5 data for the study of climate extremes under climate change?

10.5194/egusphere-egu21-12186 ◽

2021 ◽

Author(s):

Dimitri Defrance ◽

Thomas Noël ◽

Harilaos Loukos

Keyword(s):

Climate Change ◽

High Resolution ◽

General Circulation ◽

Heat Waves ◽

Surface Wind ◽

Added Value ◽

Cumulative Distribution ◽

Climate Projections ◽

Geophysical Research ◽

Near Surface

In the beginning of this century, impacts studies due to climate change were carried out directly with the outputs of the general circulation models of the Atmosphere and the Ocean (AOGCM). However, these models had very low resolutions in the order of several degrees and the climate of some areas, such as monsoon regions, was poorly reproduced. These two disadvantages make it difficult to study the evolution of extremes. Recently, more impact studies are using outputs from multiple AOGCM models that are downscaled and unbiased. The ISIMIP consortium (https://www.isimip.org/) participates in the dissemination of this practice by proposing several AOGCM models with a resolution of 0.5&#176; X 0.5&#176;.In our study, a high-resolution climate projections dataset is obtained by statistically downscaling climate projections from the CMIP5 experiment using the ERA5 reanalysis from the Copernicus Climate Change Service. This global dataset has a spatial resolution of 0.25&#176;x 0.25&#176;, comprises 21 climate models and includes 5 surface daily variables at monthly resolution: air temperature (mean, minimum, and maximum), precipitation, and mean near-surface wind speed&#160; (No&#235;l et al. accepted). This dataset is obtained by using the quantile &#8211; quantile method Cumulative Distribution Function transform (CDFt) (Vrac et al. 2012, 2016,, developed over&#160; 10 years to bias correct or downscale climate model output, and ERA5 land data as a reference . TWe propose in this communication to present the climate variability by the end of the century in terms of extreme climate indicators such as heat waves or heavy rainfall at the local/grid point level (e.g. city level). Particular attention will be paid to the magnitude of the changes as well as the associated uncertainty.&#160;ReferencesVrac, M., Drobinski, P., Merlo, A., Herrmann, M., Lavaysse, C., Li, L., & Somot, S. (2012). Dynamical and statistical downscaling of the French Mediterranean climate: uncertainty assessment.Nat. Hazards Earth Syst. Sci., 12, 2769&#8211;2784.Vrac, M., No&#235;l, T., & Vautard, R. (2016). Bias correction of precipitation through Singularity Stochastic Removal: Because occurrences matter. Journal of Geophysical Research: Atmospheres, 121(10), 5237-5258.No&#235;l, T., Loukos, H., Defrance, D., Vrac, M., & Levavasseur, G. (2020). High-resolution downscaled CMIP5 projections dataset of essential surface climate variables over the globe coherent with ERA5 reanalyses for climate change impact assessments. Data in Brief (accepted, https://doi.org/10.31223/X53W3F)

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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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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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Distribution-based scaling to improve usability of regional climate model projections for hydrological climate change impacts studies

Hydrology Research ◽

10.2166/nh.2010.004 ◽

2010 ◽

Vol 41 (3-4) ◽

pp. 211-229 ◽

Cited By ~ 137

Author(s):

Wei Yang ◽

Johan Andréasson ◽

L. Phil Graham ◽

Jonas Olsson ◽

Jörgen Rosberg ◽

...

Keyword(s):

Climate Change ◽

Climate Models ◽

Climate Model ◽

Regional Climate ◽

Climate Change Impacts ◽

Regional Climate Models ◽

Climate Projections ◽

Impact Studies ◽

Emissions Scenarios ◽

Precipitation And Temperature

As climate change could have considerable influence on hydrology and corresponding water management, appropriate climate change inputs should be used for assessing future impacts. Although the performance of regional climate models (RCMs) has improved over time, systematic model biases still constrain the direct use of RCM output for hydrological impact studies. To address this, a distribution-based scaling (DBS) approach was developed that adjusts precipitation and temperature from RCMs to better reflect observations. Statistical properties, such as daily mean, standard deviation, distribution and frequency of precipitation days, were much improved for control periods compared to direct RCM output. DBS-adjusted precipitation and temperature from two IPCC Special Report on Emissions Scenarios (SRESA1B) transient climate projections were used as inputs to the HBV hydrological model for several river basins in Sweden for the period 1961–2100. Hydrological results using DBS were compared to results with the widely-used delta change (DC) approach for impact studies. The general signal of a warmer and wetter climate was obtained using both approaches, but use of DBS identified differences between the two projections that were not seen with DC. The DBS approach is thought to better preserve the future variability produced by the RCM, improving usability for climate change impact studies.

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A regional climate modelling projection ensemble experiment – NARCliM

Geoscientific Model Development Discussions ◽

10.5194/gmdd-6-5117-2013 ◽

2013 ◽

Vol 6 (3) ◽

pp. 5117-5139 ◽

Cited By ~ 3

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.

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Deriving probabilistic based climate scenarios using pattern scaling and statistically downscaled data

Progress in Physical Geography Earth and Environment ◽

10.1177/0309133312462935 ◽

2012 ◽

Vol 37 (2) ◽

pp. 178-205

Author(s):

Rowan Fealy

Keyword(s):

Traditional Approach ◽

Rapid Development ◽

Response Rates ◽

Mean Value ◽

Distribution Functions ◽

Climate Projections ◽

Climate Modelling ◽

Climate Scenarios ◽

Temperature And Precipitation ◽

Emissions Scenarios

This paper adopts a technique common in the dynamical climate modelling literature, that of pattern scaling, and applies it to previously available statistically downscaled station level data for Ireland for two climatically relevant variables, that of temperature and precipitation. This technique allows for the rapid development of climate scenarios for additional emissions scenarios not previously available from the GCM modelling centres. Having derived the end of century (2080s) change in both these variables for four marker emissions scenarios (A1FI, A2, B2, B1), regional response rates, or the regional rate of warming per °C global warming at each station, were calculated. The estimated ranges in regional responses at each station were then compared to regional response rates for the Irish ‘grid box’ derived from a larger sample of 14 GCMs, in order to determine if the calculated response rates were illustrative of a wider suite of GCMs. A Monte Carlo (MC) resampling approach was then employed to sample regional response rates for selected stations and for different estimates of future warming. On the basis of the MC approach, probability distribution functions (pdfs) of simulated changes in temperature and precipitation were constructed and compared to the original statistically downscaled data. The methodology and results presented represent a significant contribution to the traditional approach of statistical downscaling through the development of associated likelihoods, rather than just a change in the mean value. While the methodology presented should enable the rapid development of probabilistic based climate projections, based on a limited availability of downscaled climate scenarios, caution needs to be exercised in the interpretation of the results. While they provide a basis for risk or policy assessment, estimates of the level of risk are not independent of the method employed.

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High-Resolution Climate Projections for a Densely Populated Mediterranean Region

Sustainability ◽

10.3390/su12093684 ◽

2020 ◽

Vol 12 (9) ◽

pp. 3684

Author(s):

Mohamed Salem Nashwan ◽

Shamsuddin Shahid ◽

Eun-Sung Chung

Keyword(s):

Climate Change ◽

High Resolution ◽

Bias Correction ◽

Climate Models ◽

Global Climate ◽

Global Climate Models ◽

Future Climate Change ◽

Climate Projections ◽

Near Future ◽

Far Future

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

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The influence of constrained fossil fuel emissions scenarios on climate and water resource projections

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-8-2627-2011 ◽

2011 ◽

Vol 8 (2) ◽

pp. 2627-2665 ◽

Cited By ~ 2

Author(s):

J. D. Ward ◽

A. D. Werner ◽

W. P. Nel ◽

S. Beecham

Keyword(s):

Climate Change ◽

Fossil Fuel ◽

Climate Models ◽

South Australia ◽

High Growth ◽

Climate Projections ◽

Fuel Production ◽

Emissions Scenarios ◽

Natural Climate ◽

The Impact

Abstract. Water resources planning requires long-term projections of the impact of climate change on freshwater resources. In addition to intrinsic uncertainty associated with the natural climate, projections of climate change are subject to the combined uncertainties associated with selection of emissions scenarios, GCM ensembles and downscaling techniques. In particular, unknown future greenhouse gas emissions contribute substantially to the overall uncertainty. We contend that a reduction in uncertainty is possible by refining emissions scenarios. We present a comprehensive review of the growing body of literature that challenges the assumptions underlying the high-growth emissions scenarios (widely used in climate change impact studies), and instead points to a peak and decline in fossil fuel production occurring in the 21st century. We find that the IPCC's new RCP 4.5 scenario (low-medium emissions), as well as the B1 and A1T (low emissions) marker scenarios from the IPCC's Special Report on Emissions Scenarios are broadly consistent with the majority of recent fossil fuel production forecasts, whereas the medium to high emissions scenarios generally depend upon unrealistic assumptions of future fossil fuel production. We use a simple case study of projected climate change in 2070 for the Scott Creek catchment in South Australia to demonstrate that even with the current suite of climate models, by limiting projections to the B1 scenario, both the median change and the spread of model results are reduced relative to equivalent projections under an unrealistic high emissions scenario (A1FI).

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