scholarly journals Assessment of the Canary current upwelling system in a regionally coupled climate model

2021 ◽  
Author(s):  
Ruben Vazquez ◽  
Ivan Parras-Berrocal ◽  
William Cabos ◽  
Dmitry V. Sein ◽  
Rafael Mañanes ◽  
...  
Keyword(s):  
Climate Models ◽  
Coupled Model ◽  
Horizontal Resolution ◽  
Global Climate Models ◽  
Climate Change Signal ◽  
Climate Modelling ◽  
Near Surface ◽  
Upwelling System ◽  
The Impact ◽  
Mesoscale Processes

AbstractThe Canary current upwelling is one of the major eastern boundary coastal upwelling systems in the world, bearing a high productive ecosystem and commercially important fisheries. The Canary current upwelling system (CCUS) has a large latitudinal extension, usually divided into upwelling zones with different characteristics. Eddies, filaments and other mesoscale processes are known to have an impact in the upwelling productivity, thus for a proper representation of the CCUS and high horizontal resolution are required. Here we assess the CCUS present climate in the atmosphere–ocean regionally coupled model. The regional coupled model presents a global oceanic component with increased horizontal resolution along the northwestern African coast, and its performance over the CCUS is assessed against relevant reanalysis data sets and compared with an ensemble of global climate models (GCMs) and an ensemble of atmosphere-only regional climate models (RCMs) in order to assess the role of the horizontal resolution. The coupled system reproduces the larger scale pattern of the CCUS and its latitudinal and seasonal variability over the coastal band, improving the GCMs outputs. Moreover, it shows a performance comparable to the ensemble of RCMs in representing the coastal wind stress and near-surface air temperature fields, showing the impact of the higher resolution and coupling for CCUS climate modelling. The model is able of properly reproducing mesoscale structures, being able to simulate the upwelling filaments events off Cape Ghir, which are not well represented in most of GCMs. Our results stress the ability of the regionally coupled model to reproduce the larger scale as well as mesoscale processes over the CCUS, opening the possibility to evaluate the climate change signal there with increased confidence.

scholarly journals Assessment of The Canary Current Upwelling System In A Regionally Coupled Climate Model

2021 ◽  
Author(s):  
Ruben Vazquez ◽  
Ivan Parras-Berrocal ◽  
William Cabos ◽  
Dmitry V. Sein ◽  
Rafael Mañanes ◽  
...  
Keyword(s):  
Climate Models ◽  
Coastal Upwelling ◽  
Horizontal Resolution ◽  
Global Climate Models ◽  
Temperature Fields ◽  
Climate Change Signal ◽  
Near Surface ◽  
Upwelling System ◽  
Spatial Coverage ◽  
Mesoscale Processes

Abstract The Canary current upwelling is one of the major eastern boundary coastal upwelling systems in the world, bearing a high productive ecosystem and commercially important fisheries. The Canary current upwelling system (CCUS) has a large latitudinal extension, usually divided into upwelling zones with different characteristics. Eddies, filaments and other mesoscale processes are known to have an impact in the upwelling productivity, thus for a proper representation of the CCUS a large spatial coverage and high horizontal resolution are required. Here we assess the CCUS present climate in the atmosphere-ocean regionally coupled model ROM (REMO-OASIS-MPIOM). ROM presents a global oceanic component with increased horizontal resolution along the northwestern African coast, and its performance over the CCUS is assessed against relevant reanalysis data sets and compared with an ensemble of global climate models (GCMs) and an ensemble of atmosphere-only regional climate models (RCMs) in order to assess the role of the horizontal resolution. ROM reproduces the larger scale pattern of the CCUS and its latitudinal and seasonal variability over the coastal band, improving the GCMs outputs. ROM shows a performance comparable to the ensemble of RCMs in representing the coastal wind stress and near-surface air temperature fields. ROM is able of properly reproducing mesoscale structures, being able to simulate the upwelling filaments events off Cape Ghir, which are not well represented in most of GCMs. Our results stress the ability of ROM to reproduce the larger scale as well as mesoscale processes over the CCUS, opening the possibility to evaluate the climate change signal there with increased confidence.

scholarly journals COSMO-CLM Regional Climate Simulations in the CORDEX framework: a review

2021 ◽  
Author(s):  
Silje Lund Sørland ◽  
Roman Brogli ◽  
Praveen Kumar Pothapakula ◽  
Emmanuele Russo ◽  
Jonas Van de Walle ◽  
...  
Keyword(s):  
Climate Models ◽  
Regional Climate ◽  
Parameter Tuning ◽  
Horizontal Resolution ◽  
Global Climate Models ◽  
Limited Area ◽  
Climate Projections ◽  
Climate Modelling ◽  
Near Surface ◽  
Climate Simulations

Abstract. In the last decade, the Climate Limited-area Modeling (CLM) Community has contributed to the Coordinated Re- gional Climate Downscaling Experiment (CORDEX) with an extensive set of regional climate simulations. Using several versions of the COSMO-CLM community model, ERA-Interim reanalysis and eight Global Climate Models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) were dynamically downscaled with horizontal grid spacings of 0.44° (∼50 km), 0.22° (∼25 km) and 0.11° (∼12 km) over the CORDEX domains Europe, South Asia, East Asia, Australasia and Africa. This major effort resulted in 80 regional climate simulations publicly available through the Earth System Grid Fed- eration (ESGF) web portals for use in impact studies and climate scenario assessments. Here we review the production of these simulations and assess their results in terms of mean near-surface temperature and precipitation to aid the future design of the COSMO-CLM model simulations. It is found that a domain-specific parameter tuning is beneficial, while increasing horizontal model resolution (from 50 to 25 or 12 km grid spacing) alone does not always improve the performance of the simulation. Moreover, the COSMO-CLM performance depends on the driving data. This is generally more important than the dependence on horizontal resolution, model version and configuration. Our results emphasize the importance of performing regional climate projections in a coordinated way, where guidance from both the global (GCM) and regional (RCM) climate modelling communities is needed to increase the reliability of the GCM-RCM modelling chain.

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

2013 ◽  
Vol 14 (4) ◽  
pp. 1175-1193 ◽  
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.

scholarly journals Why Do Global Climate Models Struggle to Represent Low-Level Clouds in the West African Summer Monsoon?

2017 ◽  
Vol 30 (5) ◽  
pp. 1665-1687 ◽  
Author(s):  
Lisa Hannak ◽  
Peter Knippertz ◽  
Andreas H. Fink ◽  
Anke Kniffka ◽  
Gregor Pante
Keyword(s):  
Climate Models ◽  
Global Climate ◽  
West African Monsoon ◽  
High Elevation ◽  
West African ◽  
Global Climate Models ◽  
The West ◽  
Near Surface ◽  
Low Level ◽  
The Impact

Abstract Climate models struggle to realistically represent the West African monsoon (WAM), which hinders reliable future projections and the development of adequate adaption measures. Low-level clouds over southern West Africa (5°–10°N, 8°W–8°E) during July–September are an integral part of the WAM through their effect on the surface energy balance and precipitation, but their representation in climate models has received little attention. Here 30 (20) years of output from 18 (8) models participating in phase 5 of the Coupled Model Intercomparison Project (Year of Tropical Convection) are used to identify cloud biases and their causes. Compared to ERA-Interim reanalyses, many models show large biases in low-level cloudiness of both signs and a tendency to too high elevation and too weak diurnal cycles. At the same time, these models tend to have too strong low-level jets, the impact of which is unclear because of concomitant effects on temperature and moisture advection as well as turbulent mixing. Part of the differences between the models and ERA-Interim appear to be related to the different subgrid cloud schemes used. While nighttime tendencies in temperature and humidity are broadly realistic in most models, daytime tendencies show large problems with the vertical transport of heat and moisture. Many models simulate too low near-surface relative humidities, leading to insufficient low cloud cover and abundant solar radiation, and thus a too large diurnal cycle in temperature and relative humidity. In the future, targeted model sensitivity experiments will be needed to test possible feedback mechanisms between low clouds, radiation, boundary layer dynamics, precipitation, and the WAM circulation.

scholarly journals Statistically Downscaled CMIP6 Projections Show Stronger Warming for Germany

Atmosphere ◽  
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.

scholarly journals Large sensitivity of a Greenland ice sheet model to atmospheric forcing fields

2012 ◽  
Vol 6 (2) ◽  
pp. 1037-1083 ◽  
Author(s):  
A. Quiquet ◽  
H. J. Punge ◽  
C. Ritz ◽  
X. Fettweis ◽  
M. Kageyama ◽  
...  
Keyword(s):  
General Circulation ◽  
Climate Models ◽  
Climate Model ◽  
Ice Sheets ◽  
Ice Sheet ◽  
Greenland Ice Sheet ◽  
Global Climate Models ◽  
Near Surface ◽  
Temperature And Precipitation ◽  
The Impact

Abstract. The prediction of future climate and ice sheet evolution requires coupling of ice sheet and climate models. Before proceeding to a coupled setup, we propose to analyze the impact of model simulated climate on an ice sheet. Here, we undertake this exercise for a set of regional and global climate models. Modelled near surface air temperature and precipitation are provided as upper boundary condition to the GRISLI (GRenoble Ice Shelf and Land Ice model) hybrid ice sheet model (ISM) in its Greenland configuration. After 20 kyr of simulation, the resulting ice sheets highlight the differences between the climate models. While modelled ice sheet sizes are generally comparable to the observed ones, there are considerable deviations among the ice sheets on regional scales. These can be explained by difficulties in modelling local temperature and precipitation near the coast. This is especially true in the case of global models. But the deviations of each climate model are also due to the differences in the atmospheric general circulation. In the context of coupling ice sheet and climate models, we conclude that appropriate downscaling methods will be needed and systematic corrections of the climatic variables at the interface may be required in some cases to obtain realistic results for the Greenland ice sheet (GIS).

scholarly journals Representing ozone extremes in European megacities: the importance of resolution in a global chemistry climate model

2013 ◽  
Vol 13 (10) ◽  
pp. 27423-27458
Author(s):  
Z. S. Stock ◽  
M. R. Russo ◽  
J. A. Pyle
Keyword(s):  
Atmospheric Chemistry ◽  
Urban Areas ◽  
Climate Models ◽  
Climate Model ◽  
Surface Ozone ◽  
Horizontal Resolution ◽  
Global Climate Models ◽  
Ozone Concentrations ◽  
Coarse Resolution ◽  
The Impact

Abstract. The continuing growth of the world's urban population has led to an increasing number of cities with more than 10 million inhabitants. The higher emissions of pollutants, coupled to higher population density, makes predictions of air quality in these megacities of particular importance from both a science and a policy perspective. Global climate models are typically run at coarse resolution to enable both the efficient running of long time integrations, and the ability to run multiple future climate scenarios. However, when considering surface ozone concentrations at the local scale, coarse resolution can lead to inaccuracies arising from the highly non-linear ozone chemistry and the sensitivity of ozone to the distribution of its precursors on smaller scales. In this study, we use UM-UKCA, a global atmospheric chemistry model, coupled to the UK Met Office Unified Model, to investigate the impact of model resolution on tropospheric ozone, ranging from global to local scales. We focus on the model's ability to represent the probability of high ozone concentrations in the summer and low ozone concentrations, associated with polluted megacity environments, in the winter, and how this varies with horizontal resolution. We perform time-slice integrations with two model configurations at typical climate resolution (CR, ~150 km) and at a higher resolution (HR, ~40 km). The CR configuration leads to overestimation of ozone concentrations on both regional and local scales, while it gives broadly similar results to the HR configuration on the global scale. The HR configuration is found to produce a more realistic diurnal cycle of ozone concentrations and to give a better representation of the probability density function of ozone values in urban areas such as the megacities of London and Paris. We discuss the possible causes for the observed difference in model behaviour between CR and HR configurations and estimate the relative contribution of chemical and meteorological factors at the different scales.

scholarly journals A bias-corrected CMIP5 dataset for Africa using CDF-t method. A contribution to agricultural impact studies

2017 ◽  
Author(s):  
Adjoua Moise Famien ◽  
Serge Janicot ◽  
Abe Delfin Ochou ◽  
Mathieu Vrac ◽  
Dimitri Defrance ◽  
...  
Keyword(s):  
West Africa ◽  
Air Temperature ◽  
Climate Models ◽  
Global Climate Models ◽  
Cumulative Distribution ◽  
Shortwave Radiation ◽  
Climate Change Scenarios ◽  
Data Set ◽  
Near Surface ◽  
The Impact

Abstract. The objective of this paper is to present a new data set of bias-corrected CMIP5 global climate models (GCMs) daily data over Africa. This dataset was obtained in using the Cumulative Distribution Function Transform (CDF-t) method, a method that has been applied on several regions and contexts but never on Africa. Here CDF-t is used over the period 1950–2099 combining historical runs and climate change scenarios on 6 variables, precipitation, mean near-surface air temperature, near-surface maximum air temperature, near-surface minimum air temperature, surface down-welling shortwave radiation, and wind speed, which are critical variables for agricultural purposes. Evaluation of the results is carried out over West Africa on a list of priority users-based metrics that was discussed and selected with stakeholders and on simulated yield using a crop model simulating maize growth. Bias-corrected GCMs data are compared with another available dataset of bias-corrected GCMs, and the impact of three different reference datasets on bias-corrections is also examined in details. CDF-t is very effective in removing the biases and in reducing the high inter-GCMs scattering. Differences with other bias-corrected GCMs data are mainly due to the differences between the reference datasets. This is particular true for surface down-welling shortwave radiation, which has impacts in terms of simulated maize yields. Projections of future yields over West Africa have quite different levels, depending on bias-correction method used, but they all show a similar relative decreasing trend over the 21st century.

scholarly journals COSMO-CLM regional climate simulations in the Coordinated Regional Climate Downscaling Experiment (CORDEX) framework: a review

2021 ◽  
Vol 14 (8) ◽  
pp. 5125-5154
Author(s):  
Silje Lund Sørland ◽  
Roman Brogli ◽  
Praveen Kumar Pothapakula ◽  
Emmanuele Russo ◽  
Jonas Van de Walle ◽  
...  
Keyword(s):  
Climate Models ◽  
Regional Climate ◽  
Parameter Tuning ◽  
Horizontal Resolution ◽  
Global Climate Models ◽  
Limited Area ◽  
Climate Projections ◽  
Near Surface ◽  
Climate Simulations ◽  
Climate Downscaling

Abstract. In the last decade, the Climate Limited-area Modeling Community (CLM-Community) has contributed to the Coordinated Regional Climate Downscaling Experiment (CORDEX) with an extensive set of regional climate simulations. Using several versions of the COSMO-CLM-Community model, ERA-Interim reanalysis and eight global climate models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) were dynamically downscaled with horizontal grid spacings of 0.44∘ (∼ 50 km), 0.22∘ (∼ 25 km), and 0.11∘ (∼ 12 km) over the CORDEX domains Europe, South Asia, East Asia, Australasia, and Africa. This major effort resulted in 80 regional climate simulations publicly available through the Earth System Grid Federation (ESGF) web portals for use in impact studies and climate scenario assessments. Here we review the production of these simulations and assess their results in terms of mean near-surface temperature and precipitation to aid the future design of the COSMO-CLM model simulations. It is found that a domain-specific parameter tuning is beneficial, while increasing horizontal model resolution (from 50 to 25 or 12 km grid spacing) alone does not always improve the performance of the simulation. Moreover, the COSMO-CLM performance depends on the driving data. This is generally more important than the dependence on horizontal resolution, model version, and configuration. Our results emphasize the importance of performing regional climate projections in a coordinated way, where guidance from both the global (GCM) and regional (RCM) climate modeling communities is needed to increase the reliability of the GCM–RCM modeling chain.

scholarly journals The effect of geographic sampling on evaluation of extreme precipitation in high-resolution climate models

2020 ◽  
Vol 6 (2) ◽  
pp. 115-139
Author(s):  
Mark D. Risser ◽  
Michael F. Wehner
Keyword(s):  
High Resolution ◽  
Observational Data ◽  
Climate Models ◽  
Climate Model ◽  
Horizontal Resolution ◽  
Global Climate Models ◽  
Boreal Winter ◽  
Weather Stations ◽  
High Resolution Models ◽  
The Impact

Abstract. Traditional approaches for comparing global climate models and observational data products typically fail to account for the geographic location of the underlying weather station data. For modern global high-resolution models with a horizontal resolution of tens of kilometers, this is an oversight since there are likely grid cells where the physical output of a climate model is compared with a statistically interpolated quantity instead of actual measurements of the climate system. In this paper, we quantify the impact of geographic sampling on the relative performance of high-resolution climate model representations of precipitation extremes in boreal winter (December–January–February) over the contiguous United States (CONUS), comparing model output from five early submissions to the HighResMIP subproject of the CMIP6 experiment. We find that properly accounting for the geographic sampling of weather stations can significantly change the assessment of model performance. Across the models considered, failing to account for sampling impacts the different metrics (extreme bias, spatial pattern correlation, and spatial variability) in different ways (both increasing and decreasing). We argue that the geographic sampling of weather stations should be accounted for in order to yield a more straightforward and appropriate comparison between models and observational data sets, particularly for high-resolution models with a horizontal resolution of tens of kilometers. While we focus on the CONUS in this paper, our results have important implications for other global land regions where the sampling problem is more severe.

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