scholarly journals Which Aspects of CO2 Forcing and SST Warming Cause Most Uncertainty in Projections of Tropical Rainfall Change over Land and Ocean?

2016 ◽  
Vol 29 (7) ◽  
pp. 2493-2509 ◽  
Author(s):  
Robin Chadwick

Abstract The sources of intermodel uncertainty in regional tropical rainfall projections are examined using a framework of atmosphere-only experiments. Uncertainty is dominated by model disagreement on shifts in convective regions, but the drivers of this uncertainty differ between land and ocean. Over the tropical oceans SST pattern uncertainty plays a substantial role, although it is not the only cause of uncertainty. Over land SST pattern uncertainty appears to be much less influential, and the largest source of uncertainty comes from the response to uniform SST warming, with a secondary contribution from the response to direct CO2 forcing. This may be because a larger number of processes can cause rainfall change in response to uniform SST warming than direct CO2 forcing, and so there is more potential for models to disagree. However, new experiments designed to more accurately decompose the regional climate responses of coupled models, combined with results from high-resolution climate modeling, are needed before these results can be considered robust. The pattern of present-day rainfall does not in general provide emergent constraints on future regional rainfall change. Correlations between relative humidity (RH) change and spatial shifts in convection over many land regions suggest that a proposed causal influence of RH change on dynamical rainfall change is plausible, although causality is not demonstrated here.

2016 ◽  
Vol 29 (7) ◽  
pp. 2671-2687 ◽  
Author(s):  
Shang-Min Long ◽  
Shang-Ping Xie ◽  
Wei Liu

Abstract Uncertainty in tropical rainfall projections under increasing radiative forcing is studied by using 26 models from phase 5 of the Coupled Model Intercomparison Project. Intermodel spread in projected rainfall change generally increases with interactive sea surface temperature (SST) warming in coupled models compared to atmospheric models with a common pattern of prescribed SST increase. Moisture budget analyses reveal that much of the model uncertainty in tropical rainfall projections originates from intermodel discrepancies in the dynamical contribution due to atmospheric circulation change. Intermodel singular value decomposition (SVD) analyses further show a tight coupling between the intermodel variations in SST warming pattern and circulation change in the tropics. In the zonal mean, the first SVD mode features an anomalous interhemispheric Hadley circulation, while the second mode displays an SST peak near the equator. The asymmetric mode is accompanied by a coupled pattern of wind–evaporation–SST feedback in the tropics and is further tied to interhemispheric asymmetric change in extratropical shortwave radiative flux at the top of the atmosphere. Intermodel variability in the tropical circulation change exerts a strong control on the spread in tropical cloud cover change and cloud radiative effects among models. The results indicate that understanding the coupling between the anthropogenic changes in SST pattern and atmospheric circulation holds the key to reducing uncertainties in projections of future changes in tropical rainfall and clouds.


2018 ◽  
Vol 31 (15) ◽  
pp. 5977-5995 ◽  
Author(s):  
David P. Rowell ◽  
Robin Chadwick

Understanding the causes of regional climate projection uncertainty is a critical component toward establishing reliability of these projections. Here, four complementary experimental and decomposition techniques are synthesized to begin to understand which mechanisms differ most between models. These tools include a variety of multimodel ensembles, a decomposition of rainfall into tropics-wide or region-specific processes, and a separation of within-domain versus remote contributions to regional model projection uncertainty. Three East African regions are identified and characterized by spatially coherent intermodel projection behavior, which interestingly differs from previously identified regions of coherent interannual behavior. For the “Short Rains” regions, uncertainty in projected seasonal mean rainfall change is primarily due to uncertainties in the regional response to both the uniform and pattern components of SST warming (but not uncertainties in the global mean warming itself) and a small direct CO2 impact. These primarily derive from uncertain regional dynamics over both African and remote regions, rather than globally coherent (thermo)dynamics. For the “Long Rains” region, results are similar, except that uncertain atmospheric responses to a fixed SST pattern change are a little less important, and some key regional uncertainties are primarily located beyond Africa. The latter reflects the behavior of two outlying models that experience exceptional warming in the southern subtropical oceans, from which large lower-tropospheric moisture anomalies are advected by the mean flow to contribute to exceptional increases in the Long Rains totals. Further research could lead to a useful assessment of the reliability of these exceptional projections.


2020 ◽  
Vol 45 (1) ◽  
pp. 411-444 ◽  
Author(s):  
Valéry Masson ◽  
Aude Lemonsu ◽  
Julia Hidalgo ◽  
James Voogt

Cities are particularly vulnerable to extreme weather episodes, which are expected to increase with climate change. Cities also influence their own local climate, for example, through the relative warming known as the urban heat island (UHI) effect. This review discusses urban climate features (even in complex terrain) and processes. We then present state-of-the-art methodologies on the generalization of a common urban neighborhood classification for UHI studies, as well as recent developments in observation systems and crowdsourcing approaches. We discuss new modeling paradigms pertinent to climate impact studies, with a focus on building energetics and urban vegetation. In combination with regional climate modeling, new methods benefit the variety of climate scenarios and models to provide pertinent information at urban scale. Finally, this article presents how recent research in urban climatology contributes to the global agenda on cities and climate change.


2021 ◽  
Author(s):  
Matthias Gröger ◽  
Christian Dieterich ◽  
Jari Haapala ◽  
Ha Thi Minh Ho-Hagemann ◽  
Stefan Hagemann ◽  
...  

Abstract. Non-linear responses to externally forced climate change are known to dampen or amplify the local climate impact due to complex cross compartmental feedback loops in the earth system. These feedbacks are less well represented in traditional standalone atmosphere and ocean models on which many of today's regional climate assessments rely on (e.g. EuroCordex, NOSCCA, BACC II). This promotes the development of regional climate models for the Baltic Sea region by coupling different compartments of the earth system into more comprehensive models. Coupled models more realistically represent feedback loops than the information imposed into the region by using prescribed boundary conditions, and thus, permit a higher degree of freedom. In the past, several coupled model systems have been developed for Europe and the Baltic Sea region. This article reviews recent progress of model systems that allow two way communication between atmosphere and ocean models, models for the land surface including the terrestrial biosphere, as well as wave models at the air sea interface and hydrology models for water cycle closure. However, several processes that have so far mostly been realized by one way coupling such as marine biogeochemistry, nutrient cycling and atmospheric chemistry (e.g. aerosols) are not considered here.Compared to uncoupled standalone models, coupled earth system models models can modify mean near surface air temperatures locally up to several degrees compared to their standalone atmospheric counterparts using prescribed surface boundary conditions. Over open ocean areas, the representation of small scale oceanic processes such as vertical mixing, and sea ice dynamics appear essential to accurately resolve the air sea heat exchange in the Baltic Sea region and can only be provided by online coupled high resolution ocean models. In addition, the coupling of wave models at the ocean-atmosphere interface allows a more explicit formulation of small-scale to microphysical processes with local feedbacks to water temperature and large scale processes such as oceanic upwelling. Over land, important climate feedbacks arise from dynamical terrestrial vegetation changes as well as the implementation of land use scenarios and afforestation/deforestation that further alter surface albedo, roughness length and evapotranspiration. Furthermore, a good representation of surface temperatures and roughness length over open sea and land areas is critical for the representation of climatic extremes like e.g. heavy precipitation, storms, or tropical nights, and appear to be sensitive to coupling.For the present-day climate, many coupled atmosphere-ocean and atmosphere-land surface models demonstrate added value with respect to single climate variables in particular when low quality boundary data were used in the respective standalone model. This makes coupled models a prospective tool for downscaling climate change scenarios from global climate models because these models often have large biases on the regional scale. However, the coupling of hydrology models for closing the water cycle remains problematic as the accuracy of precipitation provided by the atmosphere models is in most cases insufficient to realistically simulate the runoff to the Baltic Sea without bias adjustments.Many regional standalone ocean and atmosphere models are tuned to well represent present day climatologies rather than accurately simulate climate change. More research is necessary about how the regional climate sensitivity (e.g. the models’ response to a given change in global mean temperature) is affected by coupling and how the spread is altered in multi-model and multi-scenario ensembles of coupled models compared to uncoupled ones.


2021 ◽  
Vol 12 (3) ◽  
pp. 939-973
Author(s):  
Matthias Gröger ◽  
Christian Dieterich ◽  
Jari Haapala ◽  
Ha Thi Minh Ho-Hagemann ◽  
Stefan Hagemann ◽  
...  

Abstract. Nonlinear responses to externally forced climate change are known to dampen or amplify the local climate impact due to complex cross-compartmental feedback loops in the Earth system. These feedbacks are less well represented in the traditional stand-alone atmosphere and ocean models on which many of today's regional climate assessments rely (e.g., EURO-CORDEX, NOSCCA and BACC II). This has promoted the development of regional climate models for the Baltic Sea region by coupling different compartments of the Earth system into more comprehensive models. Coupled models more realistically represent feedback loops than the information imposed on the region by prescribed boundary conditions and, thus, permit more degrees of freedom. In the past, several coupled model systems have been developed for Europe and the Baltic Sea region. This article reviews recent progress on model systems that allow two-way communication between atmosphere and ocean models; models for the land surface, including the terrestrial biosphere; and wave models at the air–sea interface and hydrology models for water cycle closure. However, several processes that have mostly been realized by one-way coupling to date, such as marine biogeochemistry, nutrient cycling and atmospheric chemistry (e.g., aerosols), are not considered here. In contrast to uncoupled stand-alone models, coupled Earth system models can modify mean near-surface air temperatures locally by up to several degrees compared with their stand-alone atmospheric counterparts using prescribed surface boundary conditions. The representation of small-scale oceanic processes, such as vertical mixing and sea-ice dynamics, appears essential to accurately resolve the air–sea heat exchange over the Baltic Sea, and these parameters can only be provided by online coupled high-resolution ocean models. In addition, the coupling of wave models at the ocean–atmosphere interface allows for a more explicit formulation of small-scale to microphysical processes with local feedbacks to water temperature and large-scale processes such as oceanic upwelling. Over land, important climate feedbacks arise from dynamical terrestrial vegetation changes as well as the implementation of land-use scenarios and afforestation/deforestation that further alter surface albedo, roughness length and evapotranspiration. Furthermore, a good representation of surface temperatures and roughness length over open sea and land areas is critical for the representation of climatic extremes such as heavy precipitation, storms, or tropical nights (defined as nights where the daily minimum temperature does not fall below 20 ∘C), and these parameters appear to be sensitive to coupling. For the present-day climate, many coupled atmosphere–ocean and atmosphere–land surface models have demonstrated the added value of single climate variables, in particular when low-quality boundary data were used in the respective stand-alone model. This makes coupled models a prospective tool for downscaling climate change scenarios from global climate models because these models often have large biases on the regional scale. However, the coupling of hydrology models to close the water cycle remains problematic, as the accuracy of precipitation provided by atmosphere models is, in most cases, insufficient to realistically simulate the runoff to the Baltic Sea without bias adjustments. Many regional stand-alone ocean and atmosphere models are tuned to suitably represent present-day climatologies rather than to accurately simulate climate change. Therefore, more research is required into how the regional climate sensitivity (e.g., the models' response to a given change in global mean temperature) is affected by coupling and how the spread is altered in multi-model and multi-scenario ensembles of coupled models compared with uncoupled ones.


2018 ◽  
Vol 1436 (1) ◽  
pp. 98-120 ◽  
Author(s):  
Tércio Ambrizzi ◽  
Michelle Simões Reboita ◽  
Rosmeri Porfírio da Rocha ◽  
Marta Llopart

2021 ◽  
Author(s):  
Peter Hoffmann ◽  
Diana Rechid ◽  
Vanessa Reinhart ◽  
Christina Asmus ◽  
Edouard L. Davin ◽  
...  

<p>Land-use and land cover (LULC) are continuously changing due to environmental changes and anthropogenic activities. Many observational and modeling studies show that LULC changes are important drivers altering land surface feedbacks and land-atmosphere exchange processes that have substantial impact on climate on the regional and local scale. Yet, most long-term regional climate modeling studies do not account for these changes. Therefore, within the WCRP CORDEX Flagship Pilot Study LUCAS (Land Use Change Across Scales) a new workflow was developed to generate high-resolution annual land cover change time series based on past reconstructions and future projections. First, the high-resolution global land cover dataset ESA-CCI LC (~300 m resolution) is aggregated and converted to a 0.1° resolution, fractional plant functional type (PFT) dataset. Second, the land use change information from the land-use harmonized dataset (LUH2), provided at 0.25° resolution as input for CMIP6 experiments, is translated into PFT changes employing a newly developed land use translator (LUT). The new LUT was first applied to the EURO-CORDEX domain. The resulting LULC maps for past and future - the LUCAS LUC dataset - can be applied as land use forcing to the next generation RCM simulations for downscaling CMIP6 by the EURO-CORDEX community and in the framework of FPS LUCAS. The dataset includes land cover and land management practices changes important for the regional and local scale such as urbanization and irrigation. The LUCAS LUC workflow is applied to further CORDEX domains, such as Australasia and North America. The resulting past and future land cover changes will be presented, and challenges regarding the application of the new workflow to different regions will be addressed. In addition, issues related to the implementation of the dataset into different RCMs will be discussed.</p>


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