Tag: Climate Change Downscaling Techniques

Abstract

This study conducted and evaluated 44 experiments using the non-hydrostatic version of the regional climate model RegCM4 (RegCM4-NH) and an additional three experiments with RegCM version 5 (RegCM5) over Southeast Asia for the period 2010–2015. The initiative was part of the coordinated regional climate downscaling experiment—Southeast Asia (CORDEX-SEA) project, in preparation for downscaling the latest coupled model intercomparison project Phase 6 (CMIP6) global climate models (GCMs). The RegCM4-NH experiments, forced by the ERA5 reanalysis, were configured using combinations of four cumulus, three planetary boundary layer (PBL), and three explicit moisture schemes. The spatiotemporal variability of simulated 2 m-temperature and rainfall for 2010–2015 was evaluated against observational datasets. The best experiments demonstrated reasonable reproduction of observed annual cycles and spatial distribution, while many exhibited unrealistic biases. A score ranking system was implemented to objectively compare the performance of experiments, enabling the identification of top-ranked experiments for Southeast Asia. The ensemble mean of the 44 RegCM4-NH experiments exhibited commendable performance, ranking 11^th overall. Furthermore, the three additional RegCM5 experiments did not yield improved results compared to RegCM4-NH under the same physical configuration, suggesting that opting for RegCM4-NH would be a prudent choice for the CORDEX-SEA community in the forthcoming CMIP6 downscaling cycle for Southeast Asia.

Abstract

Understanding spatial and temporal characteristics and driving factors of ecological sensitivity are an essential prerequisite for effectively managing environmental changes and steering the rational use of land resources. This study employed the Analytic Hierarchy Process and Coefficient of Variation methods to calculate the weights of ten indicators from 2000 to 2018. Then, spatiotemporal change patterns of ecological sensitivity along the Sichuan–Tibet Railway were analyzed. At the same time, four individual parameters, including soil erosion, land use status, topographic factors, and climate conditions, were evaluated to create a multi-perspective understanding of the entire ecological sensitivity. The key factors affecting ecological sensitivity were explored through a geographic detector model. The results indicate that the ecological sensitivity along the Sichuan–Tibet Railway is predominantly high or moderate, with higher sensitivity observed in the western regions and lower sensitivity in the eastern regions. From 2000 to 2018, the ecological environment showed a trend of deterioration, and the spatial and temporal distribution patterns of the four parameters are closely related to the extensive ecological sensitivity. Based on the GeoDetector results, the spatial distribution of ecological sensitivity is mainly related to digital elevation model, precipitation, and air temperature. The interaction between different factors can enhance the effect on ecological sensitivity. The interaction between precipitation and Vegetation Coverage (FVC) has the largest effect.

Abstract

Heatwaves and dry spells are major climate hazards with far-reaching implications for health, economy, agriculture, and ecosystems. The frequency of compound hot and dry summers in Europe has risen in recent years. Here we present an examination of past extreme summers and compare them to future climate conditions. We use reanalysis data (2001–2022) and model data at three global warming levels: +1.2 °C, +2 °C, and +3 °C for nine selected sub-regions. Key findings indicate a significant increase in the frequency of most extreme past occurrences under 2 °C and 3 °C warming scenarios. For specific summers, the occurrence probability rises by up to 5–6 times from 2 °C to 3 °C. Moreover, our analysis reveals a notable northward shift in the climatology of hot and dry summers under 3 °C warming. The hot and dry climate observed in Eastern Europe under current conditions is anticipated to extend into substantial parts of the Baltic coast, Finland, and Scandinavia.

Abstract

Climate change affects plant growth, food production, ecosystems, sustainable socio-economic development, and human health. The different artificial intelligence models are proposed to simulate climate parameters of Jinan city in China, include artificial neural network (ANN), recurrent NN (RNN), long short-term memory neural network (LSTM), deep convolutional NN (CNN), and CNN-LSTM. These models are used to forecast six climatic factors on a monthly ahead. The climate data for 72 years (1 January 1951–31 December 2022) used in this study include monthly average atmospheric temperature, extreme minimum atmospheric temperature, extreme maximum atmospheric temperature, precipitation, average relative humidity, and sunlight hours. The time series of 12 month delayed data are used as input signals to the models. The efficiency of the proposed models are examined utilizing diverse evaluation criteria namely mean absolute error, root mean square error (RMSE), and correlation coefficient (R). The modeling result inherits that the proposed hybrid CNN-LSTM model achieves a greater accuracy than other compared models. The hybrid CNN-LSTM model significantly reduces the forecasting error compared to the models for the one month time step ahead. For instance, the RMSE values of the ANN, RNN, LSTM, CNN, and CNN-LSTM models for monthly average atmospheric temperature in the forecasting stage are 2.0669, 1.4416, 1.3482, 0.8015 and 0.6292 °C, respectively. The findings of climate simulations shows the potential of CNN-LSTM models to improve climate forecasting. Climate prediction will contribute to meteorological disaster prevention and reduction, as well as flood control and drought resistance.

Abstract

Understanding the impact of atmospheric variability on climatological mean moisture transport is crucial because moisture transport determines continental water availability as well as convective organization and resulting precipitation. Here, we analyze the mean flow and eddy components of summer moisture transport in the downwind of the Tibetan Plateau (TP), a region that is characterized by interactions between monsoon systems, extratropical circulation, and mountainous weather systems. Using 40 years of ERA5 reanalysis data and a regional WRF simulation, we determine the absolute and relative contributions of mean flow and eddy moisture transport from multi-daily to sub-daily scales. We also link these components to large-scale circulation indices, precipitation, evaporation, and mesoscale convective systems (MCSs). The results show that the largest contributions of eddies to the climatological mean moisture transport are found in the immediate downwind region of the TP. Half of the total eddy transport downwind of the TP is due to multi-daily eddy transport and the other half is due to daily to sub-daily eddy transport. Regional precipitation anomalies are dominated by the mean flow component of southerly moisture influxes which in turn are positively correlated with different South Asian summer monsoon indices and negatively correlated with the West Northern Pacific monsoon index. The eddy transport from the south is positively correlated with a lower jet latitude but does not show any significant correlations with precipitation or MCS activity, likely due to the dominant role of the mean flow moisture transport. While the relative contributions of eddies to the climatological mean moisture transport are similar in ERA5 and WRF, the correlations between moisture transport components and large-scale circulation indices are generally weaker in WRF. This suggests that the dynamical downscaling does not significantly change the role of eddy moisture transport averaged for the region, but it resolves processes that decouple the moisture transport from its large-scale forcing.

Abstract

Air pollution is a pressing issue in Delhi, with smog occurrences causing reduced visibility and various respiratory problems. A series of severe SMOG (smoke + fog) episodes between 2013 and 2017 with reduced visibility and exceptionally high PM_2.5 concentrations have been reported in Delhi especially around Diwali festival (October–November). The Smog of 2016 is referred as Great Smog of Delhi. This study examined remote sensing data from 2013 to 2017 to investigate smog episodes in Delhi during pre-Diwali, post-Diwali, and Diwali. Satellite-derived parameters viz absorbing aerosol index (AAI), aerosol optical depth (AOD), and ozone monitoring instrument (OMI) along with air pollution data and climatic parameters were used to analyze smog episodes. The results showed that during smog episodes, AOD, AAI and PM_2.5 concentrations exceeded permissible limits significantly at all stations across Delhi during the Diwali festival. The ground-based observations at different locations across Delhi and satellite data-derived datasets confirmed the severity of smog episodes. The findings indicate that burning of fire crackers coupled with agriculture stubble burning and subsequent transport of the smoke from North Western states through the Capital had a greater impact on deteriorating air quality in Delhi than local pollution, especially during unfavorable weather conditions associated with high humidity and weaker winds. The outcomes highlight the significance of remotely sensed information in identifying smog episodes and their severity in Delhi. It also underlines the necessity for efficient interventions to control air pollution, particularly amid festivals like Diwali.

Abstract

This study reveals the simulation results of a regional ocean-atmosphere coupled model (Coupled Ocean-Atmosphere-Wave Sediment Transport Modeling System, COAWST) for the Coordinated Regional Climate Downscaling Experiment (CORDEX) East Asia Phase II, which covers a period of 21 years (1989–2009) at a horizontal resolution of 25 km. The coupled simulation (CPL) and the uncoupled atmospheric simulation (UNCPL) are compared with observations to assess their capability of capturing the climatology characteristics over East Asia. The simulated sea surface temperature (SST) in CPL is generally overestimated (underestimated) in summer (winter) over most CORDEX-EA-II regions. Both CPL and UNCPL can capture the spatial patterns of seasonal air temperature over East Asia well. However, they both simulate cold biases of the winter temperature over most regions of China. CPL can reduce the cold biases over certain regions, which may be related to the increased cloud cover reflecting more longwave radiation to the ground. Additionally, CPL improves the simulation results of summer precipitation, diminishing the dry biases over northern Bay of Bengal and South China Sea. This improvement may be related to the better description of moisture transportation and precipitable water. Furthermore, The CPL experiment can better simulate the inter-annual variability of summer precipitation and the relationship between SST and precipitation, especially over the South China Sea and Philippines Sea. CPL also captures the northward propagation and amplitude of boreal summer intra-seasonal oscillation precipitation anomalies more accurately. These findings highlight the importance of ocean-atmosphere coupling processes to simulate multiscale climate changes in the CORDEX-EA-II regions.

Abstract

Climate-induced changes in hypoxia are among the most serious threats facing estuaries, which are among the most productive ecosystems on Earth. Future projections of estuarine hypoxia typically involve long-term multi-decadal continuous simulations or more computationally efficient time slice and delta methods that are restricted to short historical and future periods. We make a first comparison of these three methods by applying a linked terrestrial–estuarine model to the Chesapeake Bay, a large coastal-plain estuary in the eastern United States. Results show that the time slice approach accurately captures the behavior of the continuous approach, indicating a minimal impact of model memory. However, increases in mean annual hypoxic volume by the mid-twenty-first century simulated by the delta approach (+ 19%) are approximately twice as large as the time slice and continuous experiments (+ 9% and + 11%, respectively), indicating an important impact of changes in climate variability. Our findings suggest that system memory and projected changes in climate variability, as well as simulation length and natural variability of system hypoxia, should be considered when deciding to apply the more computationally efficient delta and time slice methods.