scholarly journals Atmospheric stability and collapse on tidally locked rocky planets

2020 ◽  
Vol 638 ◽  
pp. A77
Author(s):  
P. Auclair-Desrotour ◽  
K. Heng
Keyword(s):  
Large Scale ◽  
Scaling Laws ◽  
Climate Models ◽  
Global Climate ◽  
Atmospheric Stability ◽  
Terrestrial Planet ◽  
Global Climate Models ◽  
Collapse Pressure ◽  
Tidal Forces ◽  
Rocky Planets

Context. Over large timescales, a terrestrial planet may be driven towards spin-orbit synchronous rotation by tidal forces. In this particular configuration, the planet exhibits permanent dayside and nightside, which may induce strong day-night temperature gradients. The nightside temperature depends on the efficiency of the day-night heat redistribution and determines the stability of the atmosphere against collapse. Aims. To better constrain the atmospheric stability, climate, and surface conditions of rocky planets located in the habitable zone of their host star, it is thus crucial to understand the complex mechanism of heat redistribution. Methods. Building on early works and assuming dry thermodynamics, we developed a hierarchy of analytic models taking into account the coupling between radiative transfer, dayside convection, and large-scale atmospheric circulation in the case of slowly rotating planets. There are two types of these models: a zero-dimensional two-layer approach and a two-column radiative-convective-subsiding-upwelling model. They yield analytical solutions and scaling laws characterising the dependence of the collapse pressure on physical features, which are compared to the results obtained by early works using 3D global climate models (GCMs). Results. The analytical theory captures (i) the dependence of temperatures on atmospheric opacities and scattering in the shortwave and in the longwave, (ii) the behaviour of the collapse pressure observed in GCM simulations at low stellar fluxes that are due to the non-linear dependence of the atmospheric opacity on the longwave optical depth at the planet’s surface, (iii) the increase of stability generated by dayside sensible heating, and (iv) the decrease of stability induced by the increase of the planet size.

scholarly journals Global climatology and trends in convective environments from ERA5 and rawinsonde data

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Mateusz Taszarek ◽  
John T. Allen ◽  
Mattia Marchio ◽  
Harold E. Brooks
Keyword(s):  
Large Scale ◽  
Climate Models ◽  
Global Climate ◽  
Convective Available Potential Energy ◽  
Global Climate Models ◽  
Convective Precipitation ◽  
The Past ◽  
The Tropics ◽  
Rawinsonde Data

AbstractGlobally, thunderstorms are responsible for a significant fraction of rainfall, and in the mid-latitudes often produce extreme weather, including large hail, tornadoes and damaging winds. Despite this importance, how the global frequency of thunderstorms and their accompanying hazards has changed over the past 4 decades remains unclear. Large-scale diagnostics applied to global climate models have suggested that the frequency of thunderstorms and their intensity is likely to increase in the future. Here, we show that according to ERA5 convective available potential energy (CAPE) and convective precipitation (CP) have decreased over the tropics and subtropics with simultaneous increases in 0–6 km wind shear (BS06). Conversely, rawinsonde observations paint a different picture across the mid-latitudes with increasing CAPE and significant decreases to BS06. Differing trends and disagreement between ERA5 and rawinsondes observed over some regions suggest that results should be interpreted with caution, especially for CAPE and CP across tropics where uncertainty is the highest and reliable long-term rawinsonde observations are missing.

scholarly journals A new world natural vegetation map for global change studies

2008 ◽  
Vol 80 (2) ◽  
pp. 397-408 ◽  
Author(s):  
David M. Lapola ◽  
Marcos D. Oyama ◽  
Carlos A. Nobre ◽  
Gilvan Sampaio
Keyword(s):  
Large Scale ◽  
New World ◽  
Climate Models ◽  
Global Climate ◽  
Horizontal Resolution ◽  
Vegetation Classification ◽  
Natural Vegetation ◽  
Global Climate Models ◽  
Vegetation Map ◽  
Vegetation Maps

We developed a new world natural vegetation map at 1 degree horizontal resolution for use in global climate models. We used the Dorman and Sellers vegetation classification with inclusion of a new biome: tropical seasonal forest, which refers to both deciduous and semi-deciduous tropical forests. SSiB biogeophysical parameters values for this new biome type are presented. Under this new vegetation classification we obtained a consensus map between two global natural vegetation maps widely used in climate studies. We found that these two maps assign different biomes in ca. 1/3 of the continental grid points. To obtain a new global natural vegetation map, non-consensus areas were filled according to regional consensus based on more than 100 regional maps available on the internet. To minimize the risk of using poor quality information, the regional maps were obtained from reliable internet sources, and the filling procedure was based on the consensus among several regional maps obtained from independent sources. The new map was designed to reproduce accurately both the large-scale distribution of the main vegetation types (as it builds on two reliable global natural vegetation maps) and the regional details (as it is based on the consensus of regional maps).

scholarly journals An evaluation of the importance of spatial resolution in a global climate and hydrological model based on the Rhine and Mississippi basin

2017 ◽  
Author(s):  
Imme Benedict ◽  
Chiel C. van Heerwaarden ◽  
Albrecht H. Weerts ◽  
Wilco Hazeleger
Keyword(s):  
High Resolution ◽  
Spatial Resolution ◽  
Large Scale ◽  
Climate Models ◽  
Hydrological Cycle ◽  
Global Climate ◽  
Global Scale ◽  
Global Climate Models ◽  
Convective Processes ◽  
Parameter Values

Abstract. The hydrological cycle of river basins can be simulated by combining global climate models (GCMs) and global hydrological models (GHMs). The spatial resolution of these models is restricted by computational resources and therefore limits the processes and level of detail that can be resolved. To further improve simulations of precipitation and river-runoff on a global scale, we assess and compare the benefits of an increased resolution for a GCM and a GHM. We focus on the Rhine and Mississippi basin. Increasing the resolution of a GCM (1.125° to 0.25°) results in more realistic large-scale circulation patterns over the Rhine and an improved precipitation budget. These improvements with increased resolution are not found for the Mississippi basin, most likely because precipitation is strongly dependent on the representation of still unresolved convective processes. Increasing the resolution of vegetation and orography in the high resolution GHM (from 0.5° to 0.05°) shows no significant differences in discharge for both basins, because the hydrological processes depend highly on other parameter values that are not readily available at high resolution. Therefore, increasing the resolution of the GCM provides the most straightforward route to better results. This route works best for basins driven by large-scale precipitation, such as the Rhine basin. For basins driven by convective processes, such as the Mississippi basin, improvements are expected with even higher resolution convection permitting models.

scholarly journals Understanding Intermodel Variability in Future Projections of a Sahelian Storm Proxy and Southern Saharan Warming

2021 ◽  
Vol 34 (2) ◽  
pp. 509-525
Author(s):  
David P. Rowell ◽  
Rory G. J. Fitzpatrick ◽  
Lawrence S. Jackson ◽  
Grace Redmond
Keyword(s):  
Large Scale ◽  
Climate Models ◽  
Global Climate ◽  
Vertical Wind Shear ◽  
Global Climate Models ◽  
Tropospheric Temperature ◽  
Storm Activity ◽  
Convective Systems ◽  
The North ◽  
Projected Changes

AbstractProjected changes in the intensity of severe rain events over the North African Sahel—falling from large mesoscale convective systems—cannot be directly assessed from global climate models due to their inadequate resolution and parameterization of convection. Instead, the large-scale atmospheric drivers of these storms must be analyzed. Here we study changes in meridional lower-tropospheric temperature gradient across the Sahel (ΔTGrad), which affect storm development via zonal vertical wind shear and Saharan air layer characteristics. Projected changes in ΔTGrad vary substantially among models, adversely affecting planning decisions that need to be resilient to adverse risks, such as increased flooding. This study seeks to understand the causes of these projection uncertainties and finds three key drivers. The first is intermodel variability in remote warming, which has strongest impact on the eastern Sahel, decaying toward the west. Second, and most important, a warming–advection–circulation feedback in a narrow band along the southern Sahara varies in strength between models. Third, variations in southern Saharan evaporative anomalies weakly affect ΔTGrad, although for an outlier model these are sufficiently substantive to reduce warming here to below that of the global mean. Together these uncertain mechanisms lead to uncertain southern Saharan/northern Sahelian warming, causing the bulk of large intermodel variations in ΔTGrad. In the southern Sahel, a local negative feedback limits the contribution to uncertainties in ΔTGrad. This new knowledge of ΔTGrad projection uncertainties provides understanding that can be used, in combination with further research, to constrain projections of severe Sahelian storm activity.

scholarly journals A Bootstrap Technique for Testing the Relationship between Local-Scale Radar Observations of Cloud Occurrence and Large-Scale Atmospheric Fields

2006 ◽  
Vol 63 (11) ◽  
pp. 2813-2830 ◽  
Author(s):  
Roger Marchand ◽  
Nathaniel Beagley ◽  
Sandra E. Thompson ◽  
Thomas P. Ackerman ◽  
David M. Schultz
Keyword(s):  
Large Scale ◽  
Climate Models ◽  
Initial Conditions ◽  
Global Climate ◽  
Stable State ◽  
Local Scale ◽  
Global Climate Models ◽  
Scale Model ◽  
Synoptic Scale ◽  
Cloud Properties

Abstract A classification scheme is created to map the synoptic-scale (large scale) atmospheric state to distributions of local-scale cloud properties. This mapping is accomplished by a neural network that classifies 17 months of synoptic-scale initial conditions from the rapid update cycle forecast model into 25 different states. The corresponding data from a vertically pointing millimeter-wavelength cloud radar (from the Atmospheric Radiation Measurement Program Southern Great Plains site at Lamont, Oklahoma) are sorted into these 25 states, producing vertical profiles of cloud occurrence. The temporal stability and distinctiveness of these 25 profiles are analyzed using a bootstrap resampling technique. A stable-state-based mapping from synoptic-scale model fields to local-scale cloud properties could be useful in three ways. First, such a mapping may improve the understanding of differences in cloud properties between output from global climate models and observations by providing a physical context. Second, this mapping could be used to identify the cause of errors in the modeled distribution of clouds—whether the cause is a difference in state occurrence (the type of synoptic activity) or the misrepresentation of clouds for a particular state. Third, robust mappings could form the basis of a new statistical cloud parameterization.

scholarly journals Terrestrial Aridity and Its Response to Greenhouse Warming across CMIP5 Climate Models

2015 ◽  
Vol 28 (14) ◽  
pp. 5583-5600 ◽  
Author(s):  
Jacob Scheff ◽  
Dargan M. W. Frierson
Keyword(s):  
Land Surface ◽  
Large Scale ◽  
Climate Models ◽  
Potential Evapotranspiration ◽  
Global Climate ◽  
Aridity Index ◽  
Global Climate Models ◽  
Greenhouse Warming ◽  
Dimensionless Ratio ◽  
The Tropics

Abstract The aridity of a terrestrial climate is often quantified using the dimensionless ratio of annual precipitation (P) to annual potential evapotranspiration (PET). In this study, the climatological patterns and greenhouse warming responses of terrestrial P, Penman–Monteith PET, and are compared among 16 modern global climate models. The large-scale climatological values and implied biome types often disagree widely among models, with large systematic differences from observational estimates. In addition, the PET climatologies often differ by several tens of percent when computed using monthly versus 3-hourly inputs. With greenhouse warming, land P does not systematically increase or decrease, except at high latitudes. Therefore, because of moderate, ubiquitous PET increases, decreases (drying) are much more widespread than increases (wetting) in the tropics, subtropics, and midlatitudes in most models, confirming and expanding on earlier findings. The PET increases are also somewhat sensitive to the time resolution of the inputs, although not as systematically as for the PET climatologies. The changes in the balance between P and PET are also quantified using an alternative aridity index, the ratio , which has a one-to-one but nonlinear correspondence with . It is argued that the magnitudes of changes are more uniformly relevant than the magnitudes of changes, which tend to be much higher in wetter regions. The ratio and its changes are also found to be excellent statistical predictors of the land surface evaporative fraction and its changes.

Seasonal modulation of trapped gravity waves and their imprints on trade wind clouds

2020 ◽  
Author(s):  
Claudia Stephan
Keyword(s):  
Gravity Waves ◽  
Large Scale ◽  
Climate Models ◽  
Global Climate ◽  
Wave Theory ◽  
Internal Gravity Waves ◽  
Global Climate Models ◽  
Trade Wind ◽  
Trapped Waves ◽  
Convective Boundary

<p>Idealized simulations have shown decades ago that shallow clouds generate internal gravity waves, which under certain atmospheric background conditions become trapped inside the troposphere and influence the development of clouds. These feedbacks, which occur at horizontal scales of up to several tens of km are neither resolved, nor parameterized in traditional global climate models (GCMs), while the newest generation of GCMs is starting to resolve them. The interactions between the convective boundary layer and trapped waves have almost exclusively been studied in highly idealized frameworks and it remains unclear to what degree this coupling affects the organization of clouds and convection in the real atmosphere. Here, the coupling between clouds and trapped waves is examined in storm-resolving simulations that span the entirety of the tropical Atlantic and are initialized and forced by meteorological analyses. The coupling between clouds and trapped waves is sufficiently strong to be detected in these simulations of full complexity.  Stronger upper-tropospheric westerly winds are associated with a stronger cloud-wave coupling. In the simulations this results in a highly-organized scattered cloud field with cloud spacings of about 19 km, matching the dominant trapped wavelength. Based on the large-scale atmospheric state wave theory can reliably predict the regions and times where cloud-wave feedbacks become relevant to convective organization. Theory, the simulations and satellite imagery imply a seasonal cycle in the trapping of gravity waves. </p>

scholarly journals Downscale of future climate change scenarios applied to Recife-PE

2020 ◽  
Vol 9 (6) ◽  
pp. 361
Author(s):  
Rafaela Lisboa Costa ◽  
Heliofábio Barros Gomes ◽  
Fabrício Daniel Dos Santos Silva ◽  
Rodrigo Lins Da Rocha Júnior
Keyword(s):  
Large Scale ◽  
Climate Models ◽  
Global Climate ◽  
Reanalysis Data ◽  
Global Climate Models ◽  
Future Climate ◽  
Future Climate Change ◽  
Climate Change Scenarios ◽  
The Future ◽  
Maximum Temperatures

The objective of this work was to analyze and compare results from two generations of global climate models (GCMs) simulations for the city of Recife-PE: CMIP3 and CMIP5. Differences and similarities in historical and future climate simulations are presented for four GCMs using CMIP3 scenarios A1B and A2 and for seven CMIP5 scenarios RCP4.5 and RCP8.5. The scale reduction technique applied to GCMs scenarios is statistical downscaling, employing the same set of large-scale atmospheric variables as predictors for both sets of scenarios, differing only in the type of reanalysis data used to characterize surface variables precipitation, maximum and minimum temperatures. For CMIP3 scenarios the simulated historical climate is 1961-1990 and CMIP5 is 1979-2000, and the validation period is ten years, 1991-2000 for CMIP3 and 2001-2010 for CMIP5. However, for both the future period analyzed is 2021-2050 and 2051-2080. Validation metrics indicated superior results from the historical simulations of CMIP5 over those of CMIP3 for precipitation and minimum and similar temperatures for maximum temperatures. For the future, both CMIP3 and CMIP5 scenarios indicate reduced precipitation and increased temperatures. The potencial evapotranspiration was calculated, projected to increase in scenarios A1B and A2 of CMIP3 and with behavior similar to that observed historically in scenarios RCP4.5 and 8.5.

scholarly journals Saharan Heat Low Biases in CMIP5 Models

2017 ◽  
Vol 30 (8) ◽  
pp. 2867-2884 ◽  
Author(s):  
Ross D. Dixon ◽  
Anne Sophie Daloz ◽  
Daniel J. Vimont ◽  
Michela Biasutti
Keyword(s):  
Large Scale ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
West African Monsoon ◽  
West African ◽  
Global Climate Models ◽  
Cmip5 Models ◽  
African Climate ◽  
Heat Low

Representing the West African monsoon (WAM) is a major challenge in climate modeling because of the complex interaction between local and large-scale mechanisms. This study focuses on the representation of a key aspect of West African climate, namely the Saharan heat low (SHL), in 22 global climate models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) multimodel dataset. Comparison of the CMIP5 simulations with reanalyses shows large biases in the strength and location of the mean SHL. CMIP5 models tend to develop weaker climatological heat lows than the reanalyses and place them too far southwest. Models that place the climatological heat low farther to the north produce more mean precipitation across the Sahel, while models that place the heat low farther to the east produce stronger African easterly wave (AEW) activity. These mean-state biases are seen in model ensembles with both coupled and fixed sea surface temperatures (SSTs). The importance of SSTs on West African climate variability is well documented, but this research suggests SSTs are secondary to atmospheric biases for understanding the climatological SHL bias. SHL biases are correlated across the models to local radiative terms, large-scale tropical precipitation, and large-scale pressure and wind across the Atlantic, suggesting that local mechanisms that control the SHL may be connected to climate model biases at a much larger scale.

Downscaling Climate Information

2016 ◽  
Author(s):  
Rasmus Benestad
Keyword(s):  
Climate Change ◽  
Large Scale ◽  
Climate Models ◽  
Southern Oscillation ◽  
Global Climate ◽  
Global Climate Model ◽  
Global Climate Models ◽  
Local Climate ◽  
Skill Scores ◽  
The Right

What are the local consequences of a global climate change? This question is important for proper handling of risks associated with weather and climate. It also tacitly assumes that there is a systematic link between conditions taking place on a global scale and local effects. It is the utilization of the dependency of local climate on the global picture that is the backbone of downscaling; however, it is perhaps easiest to explain the concept of downscaling in climate research if we start asking why it is necessary. Global climate models are our best tools for computing future temperature, wind, and precipitation (or other climatological variables), but their limitations do not let them calculate local details for these quantities. It is simply not adequate to interpolate from model results. However, the models are able to predict large-scale features, such as circulation patterns, El Niño Southern Oscillation (ENSO), and the global mean temperature. The local temperature and precipitation are nevertheless related to conditions taking place over a larger surrounding region as well as local geographical features (also true, in general, for variables connected to weather/climate). This, of course, also applies to other weather elements. Downscaling makes use of systematic dependencies between local conditions and large-scale ambient phenomena in addition to including information about the effect of the local geography on the local climate. The application of downscaling can involve several different approaches. This article will discuss various downscaling strategies and methods and will elaborate on their rationale, assumptions, strengths, and weaknesses. One important issue is the presence of spontaneous natural year-to-year variations that are not necessarily directly related to the global state, but are internally generated and superimposed on the long-term climate change. These variations typically involve phenomena such as ENSO, the North Atlantic Oscillation (NAO), and the Southeast Asian monsoon, which are nonlinear and non-deterministic. We cannot predict the exact evolution of non-deterministic natural variations beyond a short time horizon. It is possible nevertheless to estimate probabilities for their future state based, for instance, on projections with models run many times with slightly different set-up, and thereby to get some information about the likelihood of future outcomes. When it comes to downscaling and predicting regional and local climate, it is important to use many global climate model predictions. Another important point is to apply proper validation to make sure the models give skillful predictions. For some downscaling approaches such as regional climate models, there usually is a need for bias adjustment due to model imperfections. This means the downscaling doesn’t get the right answer for the right reason. Some of the explanations for the presence of biases in the results may be different parameterization schemes in the driving global and the nested regional models. A final underlying question is: What can we learn from downscaling? The context for the analysis is important, as downscaling is often used to find answers to some (implicit) question and can be a means of extracting most of the relevant information concerning the local climate. It is also important to include discussions about uncertainty, model skill or shortcomings, model validation, and skill scores.

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