This study examines the response of the tropical atmospheric and oceanic circulation to increasing greenhouse gases using a coordinated set of twenty-first-century climate model experiments performed for the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4). The strength of the atmospheric overturning circulation decreases as the climate warms in all IPCC AR4 models, in a manner consistent with the thermodynamic scaling arguments of Held and Soden. The weakening occurs preferentially in the zonally asymmetric (i.e., Walker) rather than zonal-mean (i.e., Hadley) component of the tropical circulation and is shown to induce substantial changes to the thermal structure and circulation of the tropical oceans. Evidence suggests that the overall circulation weakens by decreasing the frequency of strong updrafts and increasing the frequency of weak updrafts, although the robustness of this behavior across all models cannot be confirmed because of the lack of data. As the cli...

Global Warming and the Weakening of the Tropical Circulation

Using machine learning models, remote sensing, and GIS to investigate the effects of changing climates and land uses on flood probability

Commercial success of big data has led to speculation that big-data-like reasoning could partly replace theory-based approaches in science. Big data typically has been applied to ‘small problems’, which are well-structured cases characterized by repeated evaluation of predictions. Here, we show that in climate research, intermediate categories exist between classical domain science and big data, and that big-data elements have also been applied without the possibility of repeated evaluation. Big-data elements can be useful for climate research beyond small problems if combined with more traditional approaches based on domain-specific knowledge. The biggest potential for big-data elements, we argue, lies in socioeconomic climate research. Big data is increasingly popular in many research domains. This Perspective discusses where elements of big data approaches have been employed in climate research and where combining big data with theory-driven research can be most fruitful.

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Applying big data beyond small problems in climate research

Grasslands are an important component of terrestrial ecosystems that play a crucial role in the carbon cycle and climate change. In this study, we collected aboveground biomass (AGB) data from 223 grassland quadrats distributed across the Loess Plateau from 2011 to 2013 and predicted the spatial distribution of the grassland AGB at a 100-m resolution from both meteorological station and remote sensing data (TM and MODIS) using a Random Forest (RF) algorithm. The results showed that the predicted grassland AGB on the Loess Plateau decreased from east to west. Vegetation indexes were positively correlated with grassland AGB, and the normalized difference vegetation index (NDVI) acquired from TM data was the most important predictive factor. Tussock and shrub tussock had the highest AGB, and desert steppe had the lowest. Rainfall higher than 400 m might have benefitted the grassland AGB. Compared with those obtained for the bagging, mboost and the support vector machine (SVM) models, higher values for the mean Pearson coefficient (R) and the symmetric index of agreement (λ) were obtained for the RF model, indicating that this RF model could reasonably estimate the grassland AGB (65.01%) on the Loess Plateau.

/pdf/prediction-of-aboveground-grassland-biomass-on-the-loess-4r4ekquuhs.pdf

Prediction of aboveground grassland biomass on the Loess Plateau, China, using a random forest algorithm

Detection of changes and trends in climatic variables in Bangladesh during 1988-2017.

Nowcasting of rain events using multi-frequency radiometric observations

A random forest algorithm for nowcasting of intense precipitation events

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2018JD029611

Influence of Black Carbon Aerosol on the Atmospheric Instability

Prediction of convective events using multi-frequency radiometric observations at Kolkata

The behaviors of various meteorological parameters during 1981–2010 are investigated to obtain any asymmetric variability of summertime near surface wind over Indian coastal boundaries. No significant changes were obtained in the trends of surface pressure, surface relative humidity, 2-metre temperature and surface precipitation; although, near surface wind speed is found to have significantly declined on the eastern coast with respect to the western coast during this period. Summertime surface wind speed on the eastern coast have decreased from 3.5 to 2.5 m s − 1 (7 to 5 knots) whereas 4.5 to 4 m s − 1 (9 to 8 knots) during the last three decades (statistical significance level ~ 95%). A decrease in the atmospheric instability may serve as the potential reason for the suppression of severe convective occurrences manifested by a parallel decrease in surface wind speeds over these regions. The local heating up of middle atmosphere (300–500 hPa pressure level) due to increased humidity and the difference in net heat flux over Arabian Sea and Bay of Bengal due to the variance of temperature gradient (1000–925 hPa) along the coastal boundaries might be responsible for this climatic disparity between the coastal regions of India since the last three decades. Summertime near surface wind speed projections for Indian sub-continent based on 7 best climate models, for RCP8.5 scenarios, has been calculated to show a mean increase by ~ 10–15% on the eastern coast (Eastern Ghats), ~ 1–2% on the western coasts (Western Ghats), ~ 1–5% decrease in the Indo-Gangetic Basin and ~ 3% decrease in the Gangetic West Bengal and adjoining Bangladesh.

Rohit Chakraborty

Papers

Nowcasting of rain events using multi-frequency radiometric observations

A random forest algorithm for nowcasting of intense precipitation events

Influence of Black Carbon Aerosol on the Atmospheric Instability

Prediction of convective events using multi-frequency radiometric observations at Kolkata

East-west coastal asymmetry in the summertime near surface wind speed and its projected change in future climate over the Indian region