Resolution dependence of CO2-induced Tropical Atlantic sector climate changes

Abstract A long-standing problem in state-of-the-art climate models is the Tropical Atlantic (TA) warm sea surface temperature (SST) bias, which goes along with major biases in large-scale atmospheric circulation. Here we show that TA-sector climate changes forced by increasing atmospheric carbon dioxide (CO2) levels are sensitive to model resolution. Two versions of a climate model employing greatly varying atmospheric resolution and exhibiting very different warm bias strength are compared. The version with high atmospheric resolution features a small SST bias and simulates an eastward amplified SST warming over the equatorial Atlantic, in line with the observed SST trends since the mid-20th century. On the contrary, the version with coarse atmospheric resolution exhibits a large SST bias and projects relatively uniform SST changes across the equatorial Atlantic. In both model versions, the warming pattern resembles the pattern of interannual SST variability simulated under present-day conditions. Atmospheric changes also vastly differ among the two climate model versions. In the version with small SST bias, a deep atmospheric response is simulated with a major change in the Walker circulation and strongly enhanced rainfall over the equatorial region, whereas the atmospheric response is much weaker and of rather different character in the model with large SST bias. This study suggests that higher atmospheric resolution in climate models may enhance global warming projections over the TA sector.

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Weakened impact of the Atlantic Niño on the future Guinean coast rainfall

10.5194/egusphere-egu21-1168 ◽

2021 ◽

Author(s):

Koffi Worou ◽

Hugues Goosse ◽

Thierry Fichefet

Keyword(s):

Climate Models ◽

Climate Model ◽

Rainfall Variability ◽

Coupled Model ◽

Sea Surface ◽

Boreal Summer ◽

Tropical Atlantic ◽

Equatorial Atlantic ◽

Sea Level Pressure ◽

Guinean Coast

Much of the rainfall variability in the Guinean coast area during the boreal summer is driven by the sea surface temperature (SST) variations in the eastern equatorial Atlantic, amplified by land-atmosphere interactions. This oceanic region corresponds to the center of action of the Atlantic Equatorial mode, also termed Atlantic Ni&#241;o (ATL3), which is the leading SST mode of variability in the tropical Atlantic basin. In years of positive ATL3, above normal SST conditions in the ATL3 area weaken the sea level pressure gradient between the West African lands and the ocean, which in turn reduces the monsoon flow penetration into Sahel. Subsequently, the rainfall increases over the Guinean coast area. According to observations and climate models, the relation between the Atlantic Ni&#241;o and the rainfall in coastal Guinea is stationary over the 20th century. While this relation remains unchanged over the 21st century in climate model projections, the strength of the teleconnection is reduced in a warmer climate. The weakened ATL3 effect on the rainfall over the tropical Atlantic (in years of positive ATL3) has been attributed to the stabilization of the atmosphere column above the tropical Atlantic. Analysis of historical and high anthropogenic emission scenario (the Shared Socioeconomic Pathways 5-8.5) simulations from 31 models participating in the sixth phase of the Coupled Model Intercomparison Project suggests an additional role of the Bjerkness feedback. A weakened SST amplitude related to ATL3 positive phases reduces the anomalous westerlies, which in turn increases the upwelling cooling effect on the sea surface. Both the Guinean coast region and the equatorial Atlantic experiment the projected rainfall reduction associated with ATL3, with a higher confidence over the ocean than over the coastal lands.

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Ship-based aerosol measurements in the Southern Ocean

10.5194/egusphere-egu21-12327 ◽

2021 ◽

Author(s):

Floortje van den Heuvel ◽

Thomas Lachlan-Cope ◽

Jonathan Witherstone ◽

Dean Hurren ◽

Anna Jones

Keyword(s):

Southern Ocean ◽

Large Scale ◽

Climate Models ◽

Climate Model ◽

Numerical Models ◽

Cloud Microphysics ◽

Wave Radiation ◽

Atlantic Sector ◽

Radiation Fluxes ◽

Travel Restrictions

Our limited understanding of clouds is a major source of uncertainty in climate sensitivity and climate model projections. The Southern Ocean is the largest region on Earth where climate models present large biases in short and long wave radiation fluxes which in turn affect the representation of sea surface temperatures, sea ice and ultimately large scale circulation in the Southern Hemisphere. Evidence suggests that the poor representation of mixed phase clouds at the micro- and macro scales is responsible for the model biases in this region. The Southern Ocean Clouds (SOC) project will be a multi-scale, multi-platform approach with the aim of improving understanding of aerosol and cloud microphysics in this region, and their representation in numerical models. Although this years&#8217; first SOC measurement season has suffered greatly from travel restrictions, we have installed an Optical Particle Counter (OPC) on a ship (The James Clark Ross &#8211; JCR) and recorded aerosol measurements as it was travelling through the Atlantic sector of the Southern Ocean towards the Antarctic Peninsula, and while the ship was moored at South Georgia and Port Stanley. Over the course of one month, the OPC recorded particle sizes between 0.35 and 40 micrometers every six seconds. This study will present the data from this first, rather short Antarctic SOC season. It will present the analyses of the obtained OPC data alongside satellite observations and model reanalyses in the same region.

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Mean-state dependence of future tropical-Atlantic sector climate projections

10.5194/egusphere-egu2020-10222 ◽

2020 ◽

Author(s):

Wonsun Park ◽

Mojib Latif ◽

Arielle Stela Imbol Nkwinkwa Njouodo

Keyword(s):

Global Warming ◽

Climate Model ◽

Horizontal Resolution ◽

Tropical Pacific ◽

Climate Projections ◽

Tropical Atlantic ◽

Atlantic Sector ◽

Sst Bias ◽

The Mean ◽

Mean State

Mean state and internal variability in the tropics are crucially linked to air-sea interactions. State-of-the-art climate models exhibit long-standing problems not only in simulating tropical mean climate, such as too cold sea surface temperature (SST) over the central tropical Pacific and too warm SST over the eastern tropical Pacific and Atlantic, but also with respect to seasonal and longer variability. These biases question the credibility of future climate projections with the models, and it has not been shown to date whether or how such SST biases affect the projections. Here we focus on the tropical Atlantic (TA) and investigate how the mean state influences climate projections over the region.We use two versions of the Kiel Climate Model (KCM) in global warming simulations, in which only atmosphere model resolution differs: one version carries ECHAM5 with a horizontal resolution of T42 (~2.8&#176;) and 31 vertical levels, and the other ECHAM5 with a horizontal resolution of T255 (~0.47&#176;) and 62 levels. Although only the atmospheric resolutions differ, the two KCM versions exhibit very different mean states over the tropical TA, with the higher-resolution version, among others, featuring much reduced warm SST bias over the eastern basin.The response to increasing atmospheric carbon dioxide levels is found to be sensitive to the mean state. The model employing high atmospheric resolution and featuring a small SST bias projects an eastward-amplified SST warming over the TA, consistent with the pattern of interannual SST variability simulated under present-day conditions and in line with the observed SST trends since the mid-20th century. The model employing low-resolution and exhibiting a large SST bias projects more uniform SST change. Atmospheric changes also vastly differ among the two model versions.Analysis of models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) support the KCM&#8217;s results: models with small SST bias project stronger warming over the eastern TA, while models with large SST bias either project uniform warming across the equator or largest warming in the west. This study suggests that reducing model bias may enhance global warming projections over the TA sector.

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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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Influence of the representation of convection on the mid-Holocene West African Monsoon

Climate of the Past ◽

10.5194/cp-17-1665-2021 ◽

2021 ◽

Vol 17 (4) ◽

pp. 1665-1684

Author(s):

Leonore Jungandreas ◽

Cathy Hohenegger ◽

Martin Claussen

Keyword(s):

Land Surface ◽

Large Scale ◽

Climate Models ◽

Climate Model ◽

Monsoon Season ◽

West African Monsoon ◽

West African ◽

Global Climate Models ◽

African Monsoon ◽

Monsoonal Precipitation

Abstract. Global climate models experience difficulties in simulating the northward extension of the monsoonal precipitation over north Africa during the mid-Holocene as revealed by proxy data. A common feature of these models is that they usually operate on grids that are too coarse to explicitly resolve convection, but convection is the most essential mechanism leading to precipitation in the West African Monsoon region. Here, we investigate how the representation of tropical deep convection in the ICOsahedral Nonhydrostatic (ICON) climate model affects the meridional distribution of monsoonal precipitation during the mid-Holocene by comparing regional simulations of the summer monsoon season (July to September; JAS) with parameterized and explicitly resolved convection. In the explicitly resolved convection simulation, the more localized nature of precipitation and the absence of permanent light precipitation as compared to the parameterized convection simulation is closer to expectations. However, in the JAS mean, the parameterized convection simulation produces more precipitation and extends further north than the explicitly resolved convection simulation, especially between 12 and 17∘ N. The higher precipitation rates in the parameterized convection simulation are consistent with a stronger monsoonal circulation over land. Furthermore, the atmosphere in the parameterized convection simulation is less stably stratified and notably moister. The differences in atmospheric water vapor are the result of substantial differences in the probability distribution function of precipitation and its resulting interactions with the land surface. The parametrization of convection produces light and large-scale precipitation, keeping the soils moist and supporting the development of convection. In contrast, less frequent but locally intense precipitation events lead to high amounts of runoff in the explicitly resolved convection simulations. The stronger runoff inhibits the moistening of the soil during the monsoon season and limits the amount of water available to evaporation in the explicitly resolved convection simulation.

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Improving climate model coupling through a complete mesh representation: a case study with E3SM (v1) and MOAB (v5.x)

Geoscientific Model Development ◽

10.5194/gmd-13-2355-2020 ◽

2020 ◽

Vol 13 (5) ◽

pp. 2355-2377

Author(s):

Vijay S. Mahadevan ◽

Iulian Grindeanu ◽

Robert Jacob ◽

Jason Sarich

Keyword(s):

Large Scale ◽

Climate Models ◽

Climate Model ◽

Climate Modeling ◽

System Modeling ◽

Spectral Element ◽

Scientific Workflow ◽

Model Coupling ◽

Earth System ◽

Modeling Framework

Abstract. One of the fundamental factors contributing to the spatiotemporal inaccuracy in climate modeling is the mapping of solution field data between different discretizations and numerical grids used in the coupled component models. The typical climate computational workflow involves evaluation and serialization of the remapping weights during the preprocessing step, which is then consumed by the coupled driver infrastructure during simulation to compute field projections. Tools like Earth System Modeling Framework (ESMF) (Hill et al., 2004) and TempestRemap (Ullrich et al., 2013) offer capability to generate conservative remapping weights, while the Model Coupling Toolkit (MCT) (Larson et al., 2001) that is utilized in many production climate models exposes functionality to make use of the operators to solve the coupled problem. However, such multistep processes present several hurdles in terms of the scientific workflow and impede research productivity. In order to overcome these limitations, we present a fully integrated infrastructure based on the Mesh Oriented datABase (MOAB) (Tautges et al., 2004; Mahadevan et al., 2015) library, which allows for a complete description of the numerical grids and solution data used in each submodel. Through a scalable advancing-front intersection algorithm, the supermesh of the source and target grids are computed, which is then used to assemble the high-order, conservative, and monotonicity-preserving remapping weights between discretization specifications. The Fortran-compatible interfaces in MOAB are utilized to directly link the submodels in the Energy Exascale Earth System Model (E3SM) to enable online remapping strategies in order to simplify the coupled workflow process. We demonstrate the superior computational efficiency of the remapping algorithms in comparison with other state-of-the-science tools and present strong scaling results on large-scale machines for computing remapping weights between the spectral element atmosphere and finite volume discretizations on the polygonal ocean grids.

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Regional Climate Model Emulator based on Deep Learning: Concept and First Evaluation of a Novel Hybrid Downscaling Approach

10.21203/rs.3.rs-725819/v1 ◽

2021 ◽

Author(s):

Antoine Doury ◽

Samuel Somot ◽

Sébastien Gadat ◽

Aurélien Ribes ◽

Lola Corre

Keyword(s):

Climate Change ◽

Network Architecture ◽

Large Scale ◽

Climate Models ◽

Climate Model ◽

Regional Climate ◽

Regional Climate Models ◽

Future Climate ◽

Near Surface ◽

The Way

Abstract Providing reliable information on climate change at local scale remains a challenge of first importance for impact studies and policymakers. Here, we propose a novel hybrid downscaling method combining the strengths of both empirical statistical downscaling methods and Regional Climate Models (RCMs). The aim of this tool is to enlarge the size of high-resolution RCM simulation ensembles at low cost.We build a statistical RCM-emulator by estimating the downscaling function included in the RCM. This framework allows us to learn the relationship between large-scale predictors and a local surface variable of interest over the RCM domain in present and future climate. Furthermore, the emulator relies on a neural network architecture, which grants computational efficiency. The RCM-emulator developed in this study is trained to produce daily maps of the near-surface temperature at the RCM resolution (12km). The emulator demonstrates an excellent ability to reproduce the complex spatial structure and daily variability simulated by the RCM and in particular the way the RCM refines locally the low-resolution climate patterns. Training in future climate appears to be a key feature of our emulator. Moreover, there is a huge computational benefit in running the emulator rather than the RCM, since training the emulator takes about 2 hours on GPU, and the prediction is nearly instantaneous. However, further work is needed to improve the way the RCM-emulator reproduces some of the temperature extremes, the intensity of climate change, and to extend the proposed methodology to different regions, GCMs, RCMs, and variables of interest.

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Effects of climate model radiation, humidity and wind estimates on hydrological simulations

Hydrology and Earth System Sciences ◽

10.5194/hess-16-305-2012 ◽

2012 ◽

Vol 16 (2) ◽

pp. 305-318 ◽

Cited By ~ 62

Author(s):

I. Haddeland ◽

J. Heinke ◽

F. Voß ◽

S. Eisner ◽

C. Chen ◽

...

Keyword(s):

Bias Correction ◽

Large Scale ◽

Climate Models ◽

Climate Model ◽

Meteorological Data ◽

Correction Method ◽

Hydrological Models ◽

Temperature And Precipitation ◽

Effects Of Radiation ◽

Precipitation And Temperature

Abstract. Due to biases in the output of climate models, a bias correction is often needed to make the output suitable for use in hydrological simulations. In most cases only the temperature and precipitation values are bias corrected. However, often there are also biases in other variables such as radiation, humidity and wind speed. In this study we tested to what extent it is also needed to bias correct these variables. Responses to radiation, humidity and wind estimates from two climate models for four large-scale hydrological models are analysed. For the period 1971–2000 these hydrological simulations are compared to simulations using meteorological data based on observations and reanalysis; i.e. the baseline simulation. In both forcing datasets originating from climate models precipitation and temperature are bias corrected to the baseline forcing dataset. Hence, it is only effects of radiation, humidity and wind estimates that are tested here. The direct use of climate model outputs result in substantial different evapotranspiration and runoff estimates, when compared to the baseline simulations. A simple bias correction method is implemented and tested by rerunning the hydrological models using bias corrected radiation, humidity and wind values. The results indicate that bias correction can successfully be used to match the baseline simulations. Finally, historical (1971–2000) and future (2071–2100) model simulations resulting from using bias corrected forcings are compared to the results using non-bias corrected forcings. The relative changes in simulated evapotranspiration and runoff are relatively similar for the bias corrected and non bias corrected hydrological projections, although the absolute evapotranspiration and runoff numbers are often very different. The simulated relative and absolute differences when using bias corrected and non bias corrected climate model radiation, humidity and wind values are, however, smaller than literature reported differences resulting from using bias corrected and non bias corrected climate model precipitation and temperature values.

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HYPERstream: a multi-scale framework for streamflow routing in large-scale hydrological model

Hydrology and Earth System Sciences ◽

10.5194/hess-20-2047-2016 ◽

2016 ◽

Vol 20 (5) ◽

pp. 2047-2061 ◽

Cited By ~ 5

Author(s):

Sebastiano Piccolroaz ◽

Michele Di Lazzaro ◽

Antonio Zarlenga ◽

Bruno Majone ◽

Alberto Bellin ◽

...

Keyword(s):

Large Scale ◽

Climate Models ◽

Climate Model ◽

Transfer Functions ◽

Weather Forecasting ◽

Computational Grid ◽

River Network ◽

Multi Scale ◽

Continental Scale ◽

Routing Scheme

Abstract. We present HYPERstream, an innovative streamflow routing scheme based on the width function instantaneous unit hydrograph (WFIUH) theory, which is specifically designed to facilitate coupling with weather forecasting and climate models. The proposed routing scheme preserves geomorphological dispersion of the river network when dealing with horizontal hydrological fluxes, irrespective of the computational grid size inherited from the overlaying climate model providing the meteorological forcing. This is achieved by simulating routing within the river network through suitable transfer functions obtained by applying the WFIUH theory to the desired level of detail. The underlying principle is similar to the block-effective dispersion employed in groundwater hydrology, with the transfer functions used to represent the effect on streamflow of morphological heterogeneity at scales smaller than the computational grid. Transfer functions are constructed for each grid cell with respect to the nodes of the network where streamflow is simulated, by taking advantage of the detailed morphological information contained in the digital elevation model (DEM) of the zone of interest. These characteristics make HYPERstream well suited for multi-scale applications, ranging from catchment up to continental scale, and to investigate extreme events (e.g., floods) that require an accurate description of routing through the river network. The routing scheme enjoys parsimony in the adopted parametrization and computational efficiency, leading to a dramatic reduction of the computational effort with respect to full-gridded models at comparable level of accuracy. HYPERstream is designed with a simple and flexible modular structure that allows for the selection of any rainfall-runoff model to be coupled with the routing scheme and the choice of different hillslope processes to be represented, and it makes the framework particularly suitable to massive parallelization, customization according to the specific user needs and preferences, and continuous development and improvements.

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The 1986–1989 ENSO cycle in a chemical climate model

Atmospheric Chemistry and Physics ◽

10.5194/acp-6-4669-2006 ◽

2006 ◽

Vol 6 (12) ◽

pp. 4669-4685 ◽

Cited By ~ 18

Author(s):

S. Brönnimann ◽

M. Schraner ◽

B. Müller ◽

A. Fischer ◽

D. Brunner ◽

...

Keyword(s):

North Atlantic ◽

General Circulation ◽

Climate Models ◽

Climate Model ◽

Circulation Model ◽

Enso Cycle ◽

Atlantic Sector ◽

Ensemble Simulations ◽

Chemical Climate ◽

Middle Stratosphere

Abstract. A pronounced ENSO cycle occurred from 1986 to 1989, accompanied by distinct dynamical and chemical anomalies in the global troposphere and stratosphere. Reproducing these effects with current climate models not only provides a model test but also contributes to our still limited understanding of ENSO's effect on stratosphere-troposphere coupling. We performed several sets of ensemble simulations with a chemical climate model (SOCOL) forced with global sea surface temperatures. Results were compared with observations and with large-ensemble simulations performed with an atmospheric general circulation model (MRF9). We focus our analysis on the extratropical stratosphere and its coupling with the troposphere. In this context, the circulation over the North Atlantic sector is particularly important. Relative to the La Niña winter 1989, observations for the El Niño winter 1987 show a negative North Atlantic Oscillation index with corresponding changes in temperature and precipitation patterns, a weak polar vortex, a warm Arctic middle stratosphere, negative and positive total ozone anomalies in the tropics and at middle to high latitudes, respectively, as well as anomalous upward and poleward Eliassen-Palm (EP) flux in the midlatitude lower stratosphere. Most of the tropospheric features are well reproduced in the ensemble means in both models, though the amplitudes are underestimated. In the stratosphere, the SOCOL simulations compare well with observations with respect to zonal wind, temperature, EP flux, meridional mass streamfunction, and ozone, but magnitudes are underestimated in the middle stratosphere. With respect to the mechanisms relating ENSO to stratospheric circulation, the results suggest that both, upward and poleward components of anomalous EP flux are important for obtaining the stratospheric signal and that an increase in strength of the Brewer-Dobson circulation is part of that signal.

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