Climate Modelling

2015 ◽  
pp. 439-466
Keyword(s):  
Climate Modelling

Reverse Transcribing Climate Change

2012 ◽  
Vol 34 (2) ◽  
pp. 277-293 ◽  
Author(s):  
Sam Solnick
Keyword(s):  
Climate Change ◽  
Information Theory ◽  
Climate Modelling ◽  
Economic Issues ◽  
Starting Point ◽  
Scientific Competence

This paper suggests that certain conceptual, ethical and economic issues surrounding genetics are also relevant to the challenges that climate change poses to the humanities. It takes J.H. Prynne's and Derrida's engagements with biology and information theory as a starting point to address climate modelling, emissions management, biofuels, bioengineering and the importance of scientific competence.

scholarly journals Evaluating the Urban Canopy Scheme TERRA_URB in the COSMO Model for Selected European Cities

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 237 ◽  
Author(s):  
Valeria Garbero ◽  
Massimo Milelli ◽  
Edoardo Bucchignani ◽  
Paola Mercogliano ◽  
Mikhail Varentsov ◽  
...  
Keyword(s):  
Urban Areas ◽  
Morphological Characteristics ◽  
Urban Canopy ◽  
Anthropogenic Heat ◽  
Climate Modelling ◽  
Model Framework ◽  
European Cities ◽  
Cosmo Model ◽  
Anthropogenic Heat Flux ◽  
Urban Heat

The increase in built surfaces constitutes the main reason for the formation of the Urban Heat Island (UHI), that is a metropolitan area significantly warmer than its surrounding rural areas. The urban heat islands and other urban-induced climate feedbacks may amplify heat stress and urban flooding under climate change and therefore to predict them correctly has become essential. Currently in the COSMO model, cities are represented by natural land surfaces with an increased surface roughness length and a reduced vegetation cover, but this approach is unable to correctly reproduce the UHI effect. By increasing the model resolution, a representation of the main physical processes that characterize the urban local meteorology should be addressed, in order to better forecast temperature, moisture and precipitation in urban environments. Within the COSMO Consortium a bulk parameterization scheme (TERRA_URB or TU) has been developed. It parametrizes the effects of buildings, streets and other man-made impervious surfaces on energy, moist and momentum exchanges between the surface and atmosphere, and additionally accounts for the anthropogenic heat flux as a heat source from the surface to the atmosphere. TU implements an impervious water-storage parameterization, and the Semi-empirical Urban canopy parametrization (SURY) that translates 3D urban canopy into bulk parameters. This paper presents evaluation results of the TU scheme in high-resolution simulations with a recent COSMO model version for selected European cities, namely Turin, Naples and Moscow. The key conclusion of the work is that the TU scheme in the COSMO model reasonably reproduces UHI effect and improves air temperature forecasts for all the investigated urban areas, despite each city has very different morphological characteristics. Our results highlight potential benefits of a new turbulence scheme and the representation of skin-layer temperature (for vegetation) in the model performance. Our model framework provides perspectives for enhancing urban climate modelling, although further investigations in improving model parametrizations, calibration and the use of more realistic urban canopy parameters are needed.

scholarly journals Skilful predictions of the Asian summer monsoon one year ahead

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Yuhei Takaya ◽  
Yu Kosaka ◽  
Masahiro Watanabe ◽  
Shuhei Maeda
Keyword(s):  
Long Range ◽  
Summer Monsoon ◽  
Southern Oscillation ◽  
Asian Summer Monsoon ◽  
Boreal Summer ◽  
Climate Modelling ◽  
Large Ensemble ◽  
Asian Society ◽  
Asian Summer ◽  
One Year

AbstractThe interannual variability of the Asian summer monsoon has significant impacts on Asian society. Advances in climate modelling have enabled us to make useful predictions of the seasonal Asian summer monsoon up to approximately half a year ahead, but long-range predictions remain challenging. Here, using a 52-member large ensemble hindcast experiment spanning 1980–2016, we show that a state-of-the-art climate model can predict the Asian summer monsoon and associated summer tropical cyclone activity more than one year ahead. The key to this long-range prediction is successfully simulating El Niño-Southern Oscillation evolution and realistically representing the subsequent atmosphere–ocean response in the Indian Ocean–western North Pacific in the second boreal summer of the prediction. A large ensemble size is also important for achieving a useful prediction skill, with a margin for further improvement by an even larger ensemble.

scholarly journals Application of a 1 km2 resolution model for climate change effects upon Benin and Nigeria vegetable agriculture

GEOMATICA ◽  
2019 ◽  
Vol 73 (4) ◽  
pp. 93-106
Author(s):  
Colin Minielly ◽  
O. Clement Adebooye ◽  
P.B. Irenikatche Akponikpe ◽  
Durodoluwa J. Oyedele ◽  
Dirk de Boer ◽  
...  
Keyword(s):  
Climate Change ◽  
Food Security ◽  
Field Trials ◽  
Development Planning ◽  
Climate Modelling ◽  
Intergovernmental Panel ◽  
Amaranthus Cruentus ◽  
Global Issues ◽  
Climate Change Effects

Climate change and food security are complex global issues that require multidisciplinary approaches to resolve. A nexus exists between both issues, especially in developing countries, but little prior research has successfully bridged the divide. Existing resolutions to climate change and food security are expensive and resource demanding. Climate modelling is at the forefront of climate change literature and development planning, whereas agronomy research is leading food security plans. The Benin Republic and Nigeria have grown and developed in recent years but may not have all the tools required to implement and sustain long-term food security in the face of climate change. The objective of this paper is to describe the development and outputs of a new model that bridges climate change and food security. Data from the Intergovernmental Panel on Climate Change’s 5th Regional Assessment (IPCC AR5) were combined with a biodiversity database to develop the model to derive these outputs. The model was used to demonstrate what potential impacts climate change will have on the regional food security by incorporating agronomic data from four local underutilized indigenous vegetables (Amaranthus cruentus L., Solanum macrocarpon L., Telfairia occidentalis Hook f., and Ocimum gratissimum L.). The model shows that, by 2099, there is significant uncertainty within the optimal recommendations that originated from the MicroVeg project. This suggests that MicroVeg will not have long-term success for food security unless additional options (e.g., new field trials, shifts in vegetable grown) are considered, creating the need for need for more dissemination tools.

Reprocessing the MODIS Leaf Area Index products for land surface and climate modelling

2011 ◽  
Vol 115 (5) ◽  
pp. 1171-1187 ◽  
Author(s):  
Hua Yuan ◽  
Yongjiu Dai ◽  
Zhiqiang Xiao ◽  
Duoying Ji ◽  
Wei Shangguan
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Land Surface ◽  
Climate Modelling ◽  
Area Index

scholarly journals Geographic Aspects of Temperature and Concentration Feedbacks in the Carbon Budget

2010 ◽  
Vol 23 (3) ◽  
pp. 775-784 ◽  
Author(s):  
G. J. Boer ◽  
V. Arora
Keyword(s):  
Negative Feedback ◽  
Carbon Concentration ◽  
Carbon Budget ◽  
Co2 Concentration ◽  
Atmospheric Co2 ◽  
Climate Feedback ◽  
Climate Modelling ◽  
Emission Scenarios ◽  
Temperature Feedback ◽  
Concentration Changes

Abstract The geographical distribution of feedback processes in the carbon budget is investigated in a manner that parallels that for climate feedback/sensitivity in the energy budget. Simulations for a range of emission scenarios, made with the Canadian Centre for Climate Modelling and Analysis (CCCma) earth system model (CanESM1), are the basis of the analysis. Anthropogenic CO2 emissions are concentrated in the Northern Hemisphere and provide the forcing for changes to the atmospheric carbon budget. Transports redistribute the emitted CO2 globally where local feedback processes act to enhance (positive feedback) or suppress (negative feedback) local CO2 amounts in response to changes in CO2 concentration and temperature. An increased uptake of CO2 by the land and ocean acts to counteract increased atmospheric CO2 concentrations so that “carbon–concentration” feedbacks are broadly negative over the twenty-first century. Largest values are found over land and particularly in tropical regions where CO2 acts to fertilize plant growth. Extratropical land also takes up CO2 but here the effect is limited by cooler temperatures. Oceans play a lesser negative feedback role with comparatively weak uptake associated with an increase in the atmosphere–ocean CO2 gradient rather than with oceanic biological activity. The effect of CO2-induced temperature increase is, by contrast, to increase atmospheric CO2 on average and so represents an overall positive “carbon–temperature” feedback. Although the average is positive, local regions of both positive and negative carbon–temperature feedback are seen over land as a consequence of the competition between changes in biological productivity and respiration. Positive carbon–temperature feedback is found over most tropical land while mid–high-latitude land exhibits negative feedback. There are also regions of positive and negative oceanic carbon–temperature feedback in the eastern tropical Pacific. The geographical patterns of carbon–concentration and carbon–temperature feedbacks are comparatively robust across the range of emission scenarios used, although their magnitudes are somewhat less robust and scale nonlinearly as a consequence of the large CO2 concentration changes engendered by the scenarios. The feedback patterns deduced nevertheless serve to illustrate the localized carbon feedback processes in the climate system.

Four Centuries of Summer Temperature Changes in Tierra Del Fuego: Atmospheric Drivers and Tree-Ring Reconstruction From the Southernmost Forests of the World

2021 ◽  
Author(s):  
Vladimir Matskovsky ◽  
Fidel A. Roig ◽  
Mauricio Fuentes ◽  
Irina Korneva ◽  
Diego Araneo ◽  
...  
Keyword(s):  
Tree Ring ◽  
Ring Width ◽  
Temperature Reconstruction ◽  
Summer Temperature ◽  
Temperature Variability ◽  
Tierra Del Fuego ◽  
Climate Modelling ◽  
Least Squares Regression ◽  
Nothofagus Pumilio ◽  
The World

Abstract Proxy climate records, such as those derived from tree rings, are necessary to extend relatively short instrumental meteorological observations into the past. Tierra del Fuego is the most austral territory with forests in the world, situated close to the Antarctic Peninsula, which makes this region especially interesting for paleoclimatic research. However, high-quality, high-resolution summer temperature reconstruction are lacking in the region. In this study we used 63 tree-ring width chronologies of Nothofagus pumilio and Nothofagus betuloides and partial least squares regression (PLSR) to produce annually resolved December-to-February temperature reconstruction since AD 1600 which explains up to 65% of instrumental temperature variability. We also found that observed summer temperature variability in Tierra del Fuego is primarily driven by the fluctuations of atmospheric pressure systems both in the South Atlantic and South Pacific, while it is insignificantly correlated to major hemispheric modes: ENSO and SAM. This fact makes our reconstruction important for climate modelling experiments, as it represents specific regional variability. Our reconstruction can be used for direct comparison with model outputs to better understand model limitations or to tune a model or contribute to larger scale reconstructions based on paleoclimatic data assimilation. Moreover, we showed that PLSR has improved performance over principal component regression (PCR) in the case of multiple tree-ring predictors. According to these results, PLSR may be a preferable method over PCR for the use in automated tree-ring based reconstruction approaches, akin widely used point-by-point regression.

Comparison of gridded temperature dataset of IMD and Sheffield over India

2021 ◽  
pp. 285-293
Author(s):  
Anurag Sharma ◽  
Deepak Swami ◽  
Nitin Joshi
Keyword(s):  
Spatial Distribution ◽  
Probability Distribution ◽  
Minimum Temperature ◽  
Climate Extremes ◽  
Western Himalaya ◽  
Distribution Functions ◽  
Climate Modelling ◽  
Reanalysis Dataset ◽  
Box Plots ◽  
Modelling Studies

Climate modelling and prediction studies play crucial role in identifying suitable mitigation techniques to minimize or avoid adverse consequences of climate extremes. The accurate spatially and temporally distributed temperature and rainfall dataset are key components in climate prediction studies. Reanalysis datasets provide better spatial and temporal coverage than observational datasets; therefore, reanalysis datasets are widely used for global and regional studies. However, before using the reanalysis dataset in climate modelling studies, it is crucial to compare the robustness and accuracy of the reanalysis dataset with the observational dataset. In this study, daily gridded maximum and minimum temperature datasets of Indian Meteorological Department (IMD) (1°?×?1°) and Sheffield (0.25°×0.25°) are compared using 62-years data i.e 1951-2012. The comparison is based on differences in spatial distribution pattern, probability distribution functions plots and box-plots of the respective gridded dataset. The spatial distribution of grid-wise averaged maximum and minimum temperature dataset generally compare well across pan India in both IMD and Sheffield; however, the significant differences are observed over western Himalaya (WH) and northeast (NE) region. The probability distribution of the pooled mean minimum temperature dataset of IMD is found significantly different from Sheffield using the two-sample Kolmogorov-Smirnov (KS) test. This study will be helpful for researchers who are planning to use Sheffield gridded temperature dataset for climate modelling studies.

scholarly journals Accelerating simulations using REDCHEM_v0.0 for atmospheric chemistry mechanism reduction

2018 ◽  
Vol 11 (8) ◽  
pp. 3391-3407 ◽  
Author(s):  
Zacharias Marinou Nikolaou ◽  
Jyh-Yuan Chen ◽  
Yiannis Proestos ◽  
Jos Lelieveld ◽  
Rolf Sander
Keyword(s):  
Atmospheric Chemistry ◽  
Transport Model ◽  
A Priori ◽  
Chemical Mechanism ◽  
Climate Modelling ◽  
Mechanism Reduction ◽  
Computational Speed ◽  
Speed Up ◽  
Spatio Temporal ◽  
Skeletal Mechanism

Abstract. Chemical mechanism reduction is common practice in combustion research for accelerating numerical simulations; however, there have been limited applications of this practice in atmospheric chemistry. In this study, we employ a powerful reduction method in order to produce a skeletal mechanism of an atmospheric chemistry code that is commonly used in air quality and climate modelling. The skeletal mechanism is developed using input data from a model scenario. Its performance is then evaluated both a priori against the model scenario results and a posteriori by implementing the skeletal mechanism in a chemistry transport model, namely the Weather Research and Forecasting code with Chemistry. Preliminary results, indicate a substantial increase in computational speed-up for both cases, with a minimal loss of accuracy with regards to the simulated spatio-temporal mixing ratio of the target species, which was selected to be ozone.

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