Assessment of the European climate projections as simulated by the large EURO‐CORDEX regional and global climate model ensemble

A prequel study showed that dynamic downscaling using a regional climate model (RCM) over Africa improved the Goddard Institute for Space Studies Atmosphere-Ocean Global Climate Model (GISS AOGCM: ModelE) simulation of June–September rainfall patterns over Africa. The current study applies bias corrections to the lateral and lower boundary data from the AOGCM driving the RCM, based on the comparison of a 30-year simulation to the actual climate. The analysis examines the horizontal pattern of June–September total accumulated precipitation, the time versus latitude evolution of zonal mean West Africa (WA) precipitation (showing monsoon onset timing), and the latitude versus altitude cross-section of zonal winds over WA (showing the African Easterly Jet and the Tropical Easterly Jet). The study shows that correcting for excessively warm AOGCM Atlantic sea-surface temperatures (SSTs) improves the simulation of key features, whereas applying 30-year mean bias corrections to atmospheric variables driving the RCM at the lateral boundaries does not improve the RCM simulations. We suggest that AOGCM climate projections for Africa should benefit from downscaling by nesting an RCM that has demonstrated skill in simulating African climate, driven with bias-corrected SST.

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Estimating heat stress from climate-based indicators: present-day biases and future spreads in the CMIP5 global climate model ensemble

Environmental Research Letters ◽

10.1088/1748-9326/10/8/084013 ◽

2015 ◽

Vol 10 (8) ◽

pp. 084013 ◽

Cited By ~ 34

Author(s):

Y Zhao ◽

A Ducharne ◽

B Sultan ◽

P Braconnot ◽

R Vautard

Keyword(s):

Heat Stress ◽

Climate Model ◽

Global Climate ◽

Global Climate Model ◽

Model Ensemble

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Coordinated Global and Regional Climate Modeling*

Journal of Climate ◽

10.1175/jcli-d-15-0161.1 ◽

2015 ◽

Vol 29 (1) ◽

pp. 17-35 ◽

Cited By ~ 102

Author(s):

J. F. Scinocca ◽

V. V. Kharin ◽

Y. Jiao ◽

M. W. Qian ◽

M. Lazare ◽

...

Keyword(s):

Regional Climate Model ◽

Climate Model ◽

Climate Modeling ◽

Global Climate ◽

Global Climate Model ◽

Regional Climate ◽

Value Added ◽

Regional Climate Modeling ◽

Climate Projections ◽

Climate Modelling

Abstract A new approach of coordinated global and regional climate modeling is presented. It is applied to the Canadian Centre for Climate Modelling and Analysis Regional Climate Model (CanRCM4) and its parent global climate model CanESM2. CanRCM4 was developed specifically to downscale climate predictions and climate projections made by its parent global model. The close association of a regional climate model (RCM) with a parent global climate model (GCM) offers novel avenues of model development and application that are not typically available to independent regional climate modeling centers. For example, when CanRCM4 is driven by its parent model, driving information for all of its prognostic variables is available (including aerosols and chemical species), significantly improving the quality of their simulation. Additionally, CanRCM4 can be driven by its parent model for all downscaling applications by employing a spectral nudging procedure in CanESM2 designed to constrain its evolution to follow any large-scale driving data. Coordination offers benefit to the development of physical parameterizations and provides an objective means to evaluate the scalability of such parameterizations across a range of spatial resolutions. Finally, coordinating regional and global modeling efforts helps to highlight the importance of assessing RCMs’ value added relative to their driving global models. As a first step in this direction, a framework for identifying appreciable differences in RCM versus GCM climate change results is proposed and applied to CanRCM4 and CanESM2.

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Uncertainty in Model Climate Sensitivity Traced to Representations of Cumulus Precipitation Microphysics

Journal of Climate ◽

10.1175/jcli-d-15-0191.1 ◽

2016 ◽

Vol 29 (2) ◽

pp. 543-560 ◽

Cited By ~ 67

Author(s):

Ming Zhao ◽

J.-C. Golaz ◽

I. M. Held ◽

V. Ramaswamy ◽

S.-J. Lin ◽

...

Keyword(s):

Climate Sensitivity ◽

Large Scale ◽

Climate Model ◽

Global Climate ◽

Global Climate Model ◽

Convective Precipitation ◽

Climate Projections ◽

Geophysical Fluid Dynamics Laboratory ◽

Convection Parameterization ◽

Comparable Quality

Abstract Uncertainty in equilibrium climate sensitivity impedes accurate climate projections. While the intermodel spread is known to arise primarily from differences in cloud feedback, the exact processes responsible for the spread remain unclear. To help identify some key sources of uncertainty, the authors use a developmental version of the next-generation Geophysical Fluid Dynamics Laboratory global climate model (GCM) to construct a tightly controlled set of GCMs where only the formulation of convective precipitation is changed. The different models provide simulation of present-day climatology of comparable quality compared to the model ensemble from phase 5 of CMIP (CMIP5). The authors demonstrate that model estimates of climate sensitivity can be strongly affected by the manner through which cumulus cloud condensate is converted into precipitation in a model’s convection parameterization, processes that are only crudely accounted for in GCMs. In particular, two commonly used methods for converting cumulus condensate into precipitation can lead to drastically different climate sensitivity, as estimated here with an atmosphere–land model by increasing sea surface temperatures uniformly and examining the response in the top-of-atmosphere energy balance. The effect can be quantified through a bulk convective detrainment efficiency, which measures the ability of cumulus convection to generate condensate per unit precipitation. The model differences, dominated by shortwave feedbacks, come from broad regimes ranging from large-scale ascent to subsidence regions. Given current uncertainties in representing convective precipitation microphysics and the current inability to find a clear observational constraint that favors one version of the authors’ model over the others, the implications of this ability to engineer climate sensitivity need to be considered when estimating the uncertainty in climate projections.

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Comparison of CMIP5 and CMIP6 Multi-Model Ensemble for Precipitation Downscaling Results and Observational Data: The Case of Hanjiang River Basin

Atmosphere ◽

10.3390/atmos12070867 ◽

2021 ◽

Vol 12 (7) ◽

pp. 867

Author(s):

Dong Wang ◽

Jiahong Liu ◽

Weiwei Shao ◽

Chao Mei ◽

Xin Su ◽

...

Keyword(s):

River Basin ◽

Seasonal Variations ◽

Climate Model ◽

Global Climate ◽

Global Climate Model ◽

Poor Performance ◽

Model Ensemble ◽

Downscaling Method ◽

Simulated Precipitation ◽

Hanjiang River

Evaluating global climate model (GCM) outputs is essential for accurately simulating future hydrological cycles using hydrological models. The GCM multi-model ensemble (MME) precipitation simulations of the Climate Model Intercomparison Project Phases 5 and 6 (CMIP5 and CMIP6, respectively) were spatially and temporally downscaled according to a multi-site statistical downscaling method for the Hanjiang River Basin (HRB), China. Downscaled precipitation accuracy was assessed using data collected from 14 meteorological stations in the HRB. The spatial performances, temporal performances, and seasonal variations of the downscaled CMIP5-MME and CMIP6-MME were evaluated and compared with observed data from 1970–2005. We found that the multi-site downscaling method accurately downscaled the CMIP5-MME and CMIP6-MME precipitation simulations. The downscaled precipitation of CMIP5-MME and CMIP6-MME captured the spatial pattern, temporal pattern, and seasonal variations; however, precipitation was slightly overestimated in the western and central HRB and precipitation was underestimated in the eastern HRB. The precipitation simulation ability of the downscaled CMIP6-MME relative to the downscaled CMIP5-MME improved because of reduced biases. The downscaled CMIP6-MME better simulated precipitation for most stations compared to the downscaled CMIP5-MME in all seasons except for summer. Both the downscaled CMIP5-MME and CMIP6-MME exhibit poor performance in simulating rainy days in the HRB.

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The Influence of Flow Projection Errors on Flood Hazard Estimates in Future Climate Conditions

Water ◽

10.3390/w11010049 ◽

2018 ◽

Vol 11 (1) ◽

pp. 49 ◽

Cited By ~ 5

Author(s):

Joanna Doroszkiewicz ◽

Renata Romanowicz ◽

Adam Kiczko

Keyword(s):

Climate Model ◽

Flood Hazard ◽

Global Climate ◽

Global Climate Model ◽

Future Climate ◽

Climate Projections ◽

Flood Hazards ◽

Catchment Scale ◽

Flow Routing ◽

The Impact

The continuous simulation approach to assessing the impact of climate change on future flood hazards consists of a chain of consecutive actions, starting from the choice of the global climate model (GCM) driven by an assumed CO2 emission scenario, through the downscaling of climatic forcing to a catchment scale, an estimation of flow using a hydrological model, and subsequent derivation of flood hazard maps with the help of a flow routing model. The procedure has been applied to the Biala Tarnowska catchment, Southern Poland. Future climate projections of rainfall and temperature are used as inputs to the precipitation-runoff model simulating flow in part of the catchment upstream of a modeled river reach. An application of a lumped-parameter emulator instead of a distributed flow routing model, MIKE11, substantially lowers the required computation times. A comparison of maximum inundation maps derived using both the flow routing model, MIKE11, and its lump-parameter emulator shows very small differences, which supports the feasibility of the approach. The relationship derived between maximum annual inundation areas and the upstream flow of the study can be used to assess the floodplain extent response to future climate changes. The analysis shows the large influence of the one-grid-storm error in climate projections on the return period of annual maximum inundation areas and their uncertainty bounds.

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Global Climate Model Ensemble Approaches for Future Projections of Atmospheric Rivers

Earth s Future ◽

10.1029/2019ef001249 ◽

2019 ◽

Vol 7 (10) ◽

pp. 1136-1151 ◽

Cited By ~ 12

Author(s):

E.C. Massoud ◽

V. Espinoza ◽

B. Guan ◽

D.E. Waliser

Keyword(s):

Climate Model ◽

Global Climate ◽

Global Climate Model ◽

Model Ensemble ◽

Future Projections ◽

Atmospheric Rivers

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Northern Hemisphere Stationary Waves in Future Climate Projections

Journal of Climate ◽

10.1175/2008jcli2373.1 ◽

2008 ◽

Vol 21 (23) ◽

pp. 6341-6353 ◽

Cited By ~ 37

Author(s):

Jenny Brandefelt ◽

Heiner Körnich

Keyword(s):

Northern Hemisphere ◽

Large Scale ◽

Climate Model ◽

Global Climate ◽

Global Climate Model ◽

Stationary Wave ◽

Climate Projections ◽

Stationary Waves ◽

Mean Flow ◽

Wave Response

Abstract The response of the atmospheric large-scale circulation to an enhanced greenhouse gas (GHG) forcing varies among coupled global climate model (CGCM) simulations. In this study, 16 CGCM simulations of the response of the climate system to a 1% yr−1 increase in the atmospheric CO2 concentration to quadrupling are analyzed with focus on Northern Hemisphere winter. A common signal in 14 out of the 16 simulations is an increased or unchanged stationary wave amplitude. A majority of the simulations may be categorized into one of three groups based on the GHG-induced changes in the atmospheric stationary waves. The response of the zonal mean barotropic wind is similar within each group. Fifty percent of the simulations belong to the first group, which is categorized by a stationary wave with five waves encompassing the entire NH and a strengthening of the zonal mean barotropic wind. The second and third groups, respectively consisting of three and two simulations, are characterized by a broadening and a northward shift of the zonal mean barotropic wind, respectively. A linear model of barotropic vorticity is employed to study the importance of these mean flow changes to the stationary wave response. The linear calculations indicate that the GHG-induced mean wind changes explain 50%, 4%, and 37% of the stationary wave changes in each group, respectively. Thus, for the majority of simulations the zonal mean wind changes do significantly explain the stationary wave response.

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Simulating Hydrological Impacts under Climate Change: Implications from Methodological Differences of a Pan European Assessment

Water ◽

10.3390/w10101331 ◽

2018 ◽

Vol 10 (10) ◽

pp. 1331 ◽

Cited By ~ 3

Author(s):

Aristeidis Koutroulis ◽

Lamprini Papadimitriou ◽

Manolis Grillakis ◽

Ioannis Tsanis ◽

Klaus Wyser ◽

...

Keyword(s):

Climate Change ◽

Land Surface ◽

Climate Model ◽

Global Climate ◽

Global Climate Model ◽

Surface Model ◽

Climate Projections ◽

European Continent ◽

Hydrological Changes ◽

Hydrological Impacts

The simulation of hydrological impacts in a changing climate remains one of the main challenges of the earth system sciences. Impact assessments can be, in many cases, laborious processes leading to inevitable methodological compromises that drastically affect the robustness of the conclusions. In this study we examine the implications of different CMIP5-based regional and global climate model ensembles for projections of the hydrological impacts of climate change. We compare results from three different assessments of hydrological impacts under high-end climate change (RCP8.5) across Europe, and we focus on how methodological differences affect the projections. We assess, as systematically as possible, the differences in runoff projections as simulated by a land surface model driven by three different sets of climate projections over the European continent at global warming of 1.5 °C, 2 °C and 4 °C relative to pre-industrial levels, according to the RCP8.5 concentration scenario. We find that these methodological differences lead to considerably different outputs for a number of indicators used to express different aspects of runoff. We further use a number of new global climate model experiments, with an emphasis on high resolution, to test the assumption that many of the uncertainties in regional climate and hydrological changes are driven predominantly by the prescribed sea surface temperatures (SSTs) and sea-ice concentrations (SICs) and we find that results are more sensitive to the choice of the atmosphere model compared to the driving SSTs. Finally, we combine all sources of information to identify robust patterns of hydrological changes across the European continent.

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UKCP: Understanding uncertainty in future changes in precipitation extremes at convection-permitting scale

10.5194/egusphere-egu2020-14870 ◽

2020 ◽

Author(s):

Giorgia Fosser ◽

Elizabeth Kendon ◽

Steven Chan ◽

David Stephenson

Keyword(s):

Climate Model ◽

Global Climate ◽

Global Climate Model ◽

Regional Climate ◽

Precipitation Extremes ◽

Climate Projections ◽

Climate Change Projections ◽

Convective Processes ◽

Climate Simulations ◽

The Uk

Convection-permitting models (CPMs) provide a better representation of sub-daily precipitation statistics and convective processes, both on climate and NWP time scales, mainly thanks to the possibility to switch off the parameterisation of convection. The improved realism of these models gives us greater confidence in their ability to project future changes in short-duration precipitation extremes.The first 12-member ensemble of convection-permitting climate simulations over the UK was completed within the latest updates to the UK Climate Projections (UKCP). The 20-year long CPM simulations for present-day and end of century periods are nested in an ensemble of regional climate model (RCM) simulations over Europe driven by a global climate model ensemble. In the driving ensembles, uncertain parameters in the model physics are varied within plausible bounds to sample uncertainty. Although no perturbations are applied directly to the CPMs, this project allow us to provide a first-ever estimate of uncertainty at convection-permitting scale and thus provide UK risk assessment studies with more reliable climate change projections at local and hourly scales.Here we will present results looking at the uncertainty in future changes in hourly precipitation extremes across the CPM ensemble, and how this differs from the driving RCM ensemble.

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