Kinematic and diabatic vertical velocity climatologies from a chemistry climate model

Abstract. The representation of vertical velocity in chemistry climate models is a key element for the representation of the large scale Brewer–Dobson-Circulation in the stratosphere. Here, we diagnose and compare the kinematic and diabatic vertical velocities in the ECHAM/Messy Atmospheric Chemistry (EMAC) model. The calculation of kinematic vertical velocity is based on the continuity equation, whereas diabatic vertical velocity is computed using diabatic heating rates. Annual and monthly zonal mean climatologies of vertical velocity from a 10 year simulation are provided for both, kinematic and diabatic vertical velocity representations. In general, both vertical velocity patterns show the main features of the stratospheric circulation, namely upwelling at low latitudes and downwelling at high latitudes. The main difference in the vertical velocity pattern is a more uniform structure for diabatic and a noisier structure for kinematic vertical velocity. Diabatic vertical velocities show higher absolute values both in the upwelling branch in the inner tropics and in the downwelling regions in the polar vortices. Further, there is a latitudinal shift of the tropical upwelling branch in boreal summer between the two vertical velocity representations with the tropical upwelling region in the diabatic representation shifted southward compared to the kinematic case. Furthermore, we present mean age of air climatologies from two transport schemes in EMAC using these different vertical velocities. The age of air distributions show a hemispheric difference pattern in the stratosphere with younger air in the Southern Hemisphere and older air in the Northern Hemisphere using the transport scheme with diabatic vertical velocities. Further, the age of air climatology from the transport scheme using diabatic vertical velocities shows younger mean age of air in the inner tropical upwelling branch and older mean age in the extratopical tropopause region.

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Kinematic and diabatic vertical velocity climatologies from a chemistry climate model

Atmospheric Chemistry and Physics ◽

10.5194/acp-16-6223-2016 ◽

2016 ◽

Vol 16 (10) ◽

pp. 6223-6239 ◽

Cited By ~ 6

Author(s):

Charlotte Marinke Hoppe ◽

Felix Ploeger ◽

Paul Konopka ◽

Rolf Müller

Keyword(s):

Vertical Velocity ◽

Atmospheric Chemistry ◽

Large Scale ◽

Climate Models ◽

Climate Model ◽

Boreal Summer ◽

Uniform Structure ◽

Residual Circulation ◽

Mixing Processes ◽

The Impact

Abstract. The representation of vertical velocity in chemistry climate models is a key element for the representation of the large-scale Brewer–Dobson circulation in the stratosphere. Here, we diagnose and compare the kinematic and diabatic vertical velocities in the ECHAM/Modular Earth Submodel System (MESSy) Atmospheric Chemistry (EMAC) model. The calculation of kinematic vertical velocity is based on the continuity equation, whereas diabatic vertical velocity is computed using diabatic heating rates. Annual and monthly zonal mean climatologies of vertical velocity from a 10-year simulation are provided for both kinematic and diabatic vertical velocity representations. In general, both vertical velocity patterns show the main features of the stratospheric circulation, namely, upwelling at low latitudes and downwelling at high latitudes. The main difference in the vertical velocity pattern is a more uniform structure for diabatic and a noisier structure for kinematic vertical velocity. Diabatic vertical velocities show higher absolute values both in the upwelling branch in the inner tropics and in the downwelling regions in the polar vortices. Further, there is a latitudinal shift of the tropical upwelling branch in boreal summer between the two vertical velocity representations with the tropical upwelling region in the diabatic representation shifted southward compared to the kinematic case. Furthermore, we present mean age of air climatologies from two transport schemes in EMAC using these different vertical velocities and analyze the impact of residual circulation and mixing processes on the age of air. The age of air distributions show a hemispheric difference pattern in the stratosphere with younger air in the Southern Hemisphere and older air in the Northern Hemisphere using the transport scheme with diabatic vertical velocities. Further, the age of air climatology from the transport scheme using diabatic vertical velocities shows a younger mean age of air in the inner tropical upwelling branch and an older mean age in the extratropical tropopause region.

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ATTILA 4.0: Lagrangian advective and convective transport of passive tracers within the ECHAM5/MESSy (2.53.0) chemistry–climate model

Geoscientific Model Development ◽

10.5194/gmd-12-1991-2019 ◽

2019 ◽

Vol 12 (5) ◽

pp. 1991-2008 ◽

Cited By ~ 4

Author(s):

Sabine Brinkop ◽

Patrick Jöckel

Keyword(s):

Vertical Velocity ◽

Atmospheric Chemistry ◽

Climate Model ◽

Grid Point ◽

Convective Transport ◽

Lagrangian Model ◽

Tracer Transport ◽

Mass Fluxes ◽

Tropopause Region ◽

Passive Tracers

Abstract. We have extended ATTILA (Atmospheric Tracer Transport in a LAgrangian model), a Lagrangian tracer transport scheme, which is online coupled to the global ECHAM/MESSy Atmospheric Chemistry (EMAC) model, with a combination of newly developed and modified physical routines and new diagnostic and infrastructure submodels. The new physical routines comprise a parameterisation for Lagrangian convection, a formulation of diabatic vertical velocity, and the new grid-point submodel LGTMIX to calculate the mixing of compounds in Lagrangian representation. The new infrastructure routines simplify the transformation between grid-point (GP) and Lagrangian (LG) space in a parallel computing environment. The new submodel LGVFLUX is a useful diagnostic tool to calculate online vertical mass fluxes through horizontal surfaces. The submodel DRADON was extended to account for emissions and changes of 222Rn on Lagrangian parcels. To evaluate the new physical routines, two simulations in free-running mode with prescribed sea surface temperatures were performed with EMAC–ATTILA in T42L47MA resolution from 1950 to 2010. The results show an improvement of the tracer transport into and within the stratosphere when the diabatic vertical velocity is used for vertical advection in ATTILA instead of the standard kinematic vertical velocity. In particular, the age-of-air distribution is more in accordance with observations. The global tropospheric distribution of 222Rn, however, is simulated in agreement with available observations and with the results from EMAC in grid space for both Lagrangian systems. Additional sensitivity studies reveal an effect of inter-parcel mixing on the age of air in the tropopause region and the stratosphere, but there is no significant effect for the troposphere.

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ATTILA 4.0: Lagrangian Advective and Convective Transport of Passive Tracers within the ECHAM5/MESSy (2.53.0) Chemistry Climate Model

10.5194/gmd-2018-302 ◽

2019 ◽

Author(s):

Sabine Brinkop ◽

Patrick Jöckel

Keyword(s):

Vertical Velocity ◽

Atmospheric Chemistry ◽

Climate Model ◽

Grid Point ◽

Convective Transport ◽

Lagrangian Model ◽

Tracer Transport ◽

Mass Fluxes ◽

Tropopause Region ◽

On Line

Abstract. We have extended ATTILA (Atmospheric Tracer Transport in a LAgrangian model), a Lagrangian tracer transport scheme, which is on-line coupled to the global ECHAM/MESSy Atmospheric Chemistry (EMAC) Climate model, with a combination of newly developed and modified physical routines, and new diagnostic and infrastructure submodels. The new physical routines comprise a parametrisation for Lagrangian convection, a formulation of diabatic vertical velocity, and the new grid-point submodel LGTMIX to calculate the mixing of compounds in Lagrangian representation. The new infrastructure routines simplify the transformation between grid-point (GP) and Lagrangian (LG) space in a parallel computing environment. The new submodel LGVFLUX is a useful diagnostic tool to calculate on-line vertical mass-fluxes through horizontal surfaces. The submodel DRADON was extended to account for emissions and changes of 222Radon on Lagrangian parcels. To evaluate the new physical routines, two simulations in free-running mode with prescribed sea surface temperatures were performed with EMAC-ATTILA in T42L47MA resolution from 1950 to 2010. The results show an improvement of the tracer transport into and within the stratosphere, when the diabatic vertical velocity is used for vertical advection in ATTILA instead of the standard kinematic vertical velocity. Especially the age-of-air distribution is more in accordance with observations. The global tropospheric distribution of 222Radon, however, is simulated in agreement with available observations and with the results from EMAC in grid-space for both Lagrangian systems. Additional sensitivity studies reveal an effect of the inter-parcel mixing on the age-of-air in the tropopause region and the stratosphere, but no significant effect for the troposphere.

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Relationship between Shortwave Cloud Radiative Forcing and Local Meteorological Variables Compared in Observations and Several Global Climate Models

Journal of Climate ◽

10.1175/jcli3875.1 ◽

2006 ◽

Vol 19 (17) ◽

pp. 4344-4359 ◽

Cited By ~ 12

Author(s):

Markus Stowasser ◽

Kevin Hamilton

Keyword(s):

Relative Humidity ◽

Vertical Velocity ◽

Radiative Forcing ◽

Large Scale ◽

Climate Models ◽

Grid Point ◽

Global Climate Models ◽

Coupled Models ◽

Cloud Radiative Forcing ◽

Cloud Forcing

Abstract The relations between local monthly mean shortwave cloud radiative forcing and aspects of the resolved-scale meteorological fields are investigated in hindcast simulations performed with 12 of the global coupled models included in the model intercomparison conducted as part of the preparation for Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4). In particular, the connection of the cloud forcing over tropical and subtropical ocean areas with resolved midtropospheric vertical velocity and with lower-level relative humidity are investigated and compared among the models. The model results are also compared with observational determinations of the same relationships using satellite data for the cloud forcing and global reanalysis products for the vertical velocity and humidity fields. In the analysis the geographical variability in the long-term mean among all grid points and the interannual variability of the monthly mean at each grid point are considered separately. The shortwave cloud radiative feedback (SWCRF) plays a crucial role in determining the predicted response to large-scale climate forcing (such as from increased greenhouse gas concentrations), and it is thus important to test how the cloud representations in current climate models respond to unforced variability. Overall there is considerable variation among the results for the various models, and all models show some substantial differences from the comparable observed results. The most notable deficiency is a weak representation of the cloud radiative response to variations in vertical velocity in cases of strong ascending or strong descending motions. While the models generally perform better in regimes with only modest upward or downward motions, even in these regimes there is considerable variation among the models in the dependence of SWCRF on vertical velocity. The largest differences between models and observations when SWCRF values are stratified by relative humidity are found in either very moist or very dry regimes. Thus, the largest errors in the model simulations of cloud forcing are prone to be in the western Pacific warm pool area, which is characterized by very moist strong upward currents, and in the rather dry regions where the flow is dominated by descending mean motions.

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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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Cluster-based analysis of multi-model climate ensembles

Geoscientific Model Development ◽

10.5194/gmd-11-2033-2018 ◽

2018 ◽

Vol 11 (6) ◽

pp. 2033-2048 ◽

Cited By ~ 1

Author(s):

Richard Hyde ◽

Ryan Hossaini ◽

Amber A. Leeson

Keyword(s):

Atmospheric Chemistry ◽

Tropospheric Ozone ◽

Climate Models ◽

Climate Model ◽

Complex Data ◽

Similar Data ◽

Climate Data ◽

Efficient Manner ◽

Clustering Techniques ◽

Insight Into

Abstract. Clustering – the automated grouping of similar data – can provide powerful and unique insight into large and complex data sets, in a fast and computationally efficient manner. While clustering has been used in a variety of fields (from medical image processing to economics), its application within atmospheric science has been fairly limited to date, and the potential benefits of the application of advanced clustering techniques to climate data (both model output and observations) has yet to be fully realised. In this paper, we explore the specific application of clustering to a multi-model climate ensemble. We hypothesise that clustering techniques can provide (a) a flexible, data-driven method of testing model–observation agreement and (b) a mechanism with which to identify model development priorities. We focus our analysis on chemistry–climate model (CCM) output of tropospheric ozone – an important greenhouse gas – from the recent Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP). Tropospheric column ozone from the ACCMIP ensemble was clustered using the Data Density based Clustering (DDC) algorithm. We find that a multi-model mean (MMM) calculated using members of the most-populous cluster identified at each location offers a reduction of up to ∼ 20 % in the global absolute mean bias between the MMM and an observed satellite-based tropospheric ozone climatology, with respect to a simple, all-model MMM. On a spatial basis, the bias is reduced at ∼ 62 % of all locations, with the largest bias reductions occurring in the Northern Hemisphere – where ozone concentrations are relatively large. However, the bias is unchanged at 9 % of all locations and increases at 29 %, particularly in the Southern Hemisphere. The latter demonstrates that although cluster-based subsampling acts to remove outlier model data, such data may in fact be closer to observed values in some locations. We further demonstrate that clustering can provide a viable and useful framework in which to assess and visualise model spread, offering insight into geographical areas of agreement among models and a measure of diversity across an ensemble. Finally, we discuss caveats of the clustering techniques and note that while we have focused on tropospheric ozone, the principles underlying the cluster-based MMMs are applicable to other prognostic variables from climate models.

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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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The last interglacial (Eemian) climate simulated by LOVECLIM and CCSM3

Climate of the Past Discussions ◽

10.5194/cpd-8-5293-2012 ◽

2012 ◽

Vol 8 (5) ◽

pp. 5293-5340 ◽

Cited By ~ 2

Author(s):

I. Nikolova ◽

Q. Yin ◽

A. Berger ◽

U. K. Singh ◽

M. P. Karami

Keyword(s):

Large Scale ◽

Climate Models ◽

Hydrological Cycle ◽

The Arctic ◽

Boreal Summer ◽

Interglacial Period ◽

Last Interglacial ◽

Proxy Data ◽

Vegetation Feedback ◽

Depth Analysis

Abstract. This paper presents a detailed analysis of the climate of the last interglacial simulated by two climate models of different complexities, LOVECLIM and CCSM3. The simulated surface temperature, hydrological cycle, vegetation and ENSO variability during the last interglacial are analyzed through the comparison with the simulated Pre-Industrial (PI) climate. In both models, the last interglacial period is characterized by a significant warming (cooling) over almost all the continents during boreal summer (winter) leading to a largely increased (reduced) seasonal contrast in the northern (southern) hemisphere. This is mainly due to the much higher (lower) insolation received by the whole Earth in boreal summer (winter) during this interglacial. The arctic is warmer than PI through the whole year, resulting from its much higher summer insolation and its remnant effect in the following fall-winter through the interactions between atmosphere, ocean and sea ice. In the tropical Pacific, the change in the SST annual cycle is suggested to be related to a minor shift towards an El Nino, slightly stronger for MIS-5 than for PI. Intensified African monsoon and vegetation feedback are responsible for the cooling during summer in North Africa and Arabian Peninsula. Over India precipitation maximum is found further west, while in Africa the precipitation maximum migrates further north. Trees and grassland expand north in Sahel/Sahara. A mix of forest and grassland occupies continents and expand deep in the high northern latitudes. Desert areas reduce significantly in Northern Hemisphere, but increase in North Australia. The simulated large-scale climate change during the last interglacial compares reasonably well with proxy data, giving credit to both models and reconstructions. However, discrepancies exist at some regional scales between the two models, indicating the necessity of more in depth analysis of the models and comparisons with proxy data.

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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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