Tag: Climate Change Downscaling Techniques

Abstract

As renewable energy generation increases, the impacts of weather and climate on energy generation and demand become critical to the reliability of the energy system. However, these impacts are often overlooked. Global climate models (GCMs) can be used to understand possible changes to our climate, but their coarse resolution makes them difficult to use in energy system modelling. Here we present open-source generative machine learning methods that produce meteorological data at a nominal spatial resolution of 4 km at an hourly frequency based on inputs from 100 km daily-average GCM data. These methods run 40 times faster than traditional downscaling methods and produce data that have high-resolution spatial and temporal attributes similar to historical datasets. We demonstrate that these methods can be used to downscale projected changes in wind, solar and temperature variables across multiple GCMs including projections for more frequent low-wind and high-temperature events in the Eastern United States.

Abstract

In 2022, a catastrophic flood triggered by the extreme precipitation in Sind Province, Pakistan. To better understand the comprehensive response of water vapor, rainfall, topography, and flood, the source of water vapor for the flood was calculated by the NCAR Command Language (NCL) application. Simultaneously, the Global Precipitation Measurement (GPM) data was collected from NASA for overlay analysis with water vapor observations. In addition, a digital elevation model (DEM) was also obtained to analyze the impact of topography on flood inundation. Importantly, multi Sentinel-1 data was used to monitor the long-term changes in flood inundation area. The extreme precipitation is dominated by water vapor continue transferred by southwest monsoon, especially impacted by the occurrence of cyclone. Simultaneously, influenced by the steep terrain that located in the north and west of Pakistan, the extreme precipitation first occurred in Islamabad and its adjacent area, subsequently in Punjab Province, and finally concentrated in Sind Province. The surface runoff induced by rainstorm converged in the junction of Sind and Punjab Province with the pattern of fire hose effect. Subsequently, the flood in Indus River in the Sind Province overflow into the low-lying area along the bank of Indus River due to the terrain of Indus River in these regions has the characteristics of over ground river, and the flood flow capacity is lower than that in northern of Pakistan. In addition, the long-term changes in the flood inundation area can be summarized into four stages: increase slowly period (In June), increase slightly period (In July), increase rapidly period (Between August and the beginning of September), rapidly decline period (After September 15, 2022). Importantly, a conceptual model of disaster caused by the fire pipe effect is summarized based on the comprehensive response of water vapor, rainfall, and topography.

Abstract

In 2022, a catastrophic flood triggered by the extreme precipitation in Sind Province, Pakistan. To better understand the comprehensive response of water vapor, rainfall, topography, and flood, the source of water vapor for the flood was calculated by the NCAR Command Language (NCL) application. Simultaneously, the Global Precipitation Measurement (GPM) data was collected from NASA for overlay analysis with water vapor observations. In addition, a digital elevation model (DEM) was also obtained to analyze the impact of topography on flood inundation. Importantly, multi Sentinel-1 data was used to monitor the long-term changes in flood inundation area. The extreme precipitation is dominated by water vapor continue transferred by southwest monsoon, especially impacted by the occurrence of cyclone. Simultaneously, influenced by the steep terrain that located in the north and west of Pakistan, the extreme precipitation first occurred in Islamabad and its adjacent area, subsequently in Punjab Province, and finally concentrated in Sind Province. The surface runoff induced by rainstorm converged in the junction of Sind and Punjab Province with the pattern of fire hose effect. Subsequently, the flood in Indus River in the Sind Province overflow into the low-lying area along the bank of Indus River due to the terrain of Indus River in these regions has the characteristics of over ground river, and the flood flow capacity is lower than that in northern of Pakistan. In addition, the long-term changes in the flood inundation area can be summarized into four stages: increase slowly period (In June), increase slightly period (In July), increase rapidly period (Between August and the beginning of September), rapidly decline period (After September 15, 2022). Importantly, a conceptual model of disaster caused by the fire pipe effect is summarized based on the comprehensive response of water vapor, rainfall, and topography.

Abstract

Modeling monthly rainfall erosivity is vital to the optimization of measures to control soil erosion. Rain gauge data combined with satellite observations can aid in enhancing rainfall erosivity estimations. Here, we presented a framework which utilized Geographically Weighted Regression approach to model global monthly rainfall erosivity. The framework integrates long-term (2001–2020) mean annual rainfall erosivity estimates from IMERG (Global Precipitation Measurement (GPM) mission’s Integrated Multi-satellitE Retrievals for GPM) with station data from GloREDa (Global Rainfall Erosivity Database, n = 3,286 stations). The merged mean annual rainfall erosivity was disaggregated into mean monthly values based on monthly rainfall erosivity fractions derived from the original IMERG data. Global mean monthly rainfall erosivity was distinctly seasonal; erosivity peaked at ~ 200 MJ mm ha⁻¹ h⁻¹ month⁻¹ in June–August over the Northern Hemisphere and ~ 700 MJ mm ha⁻¹ h⁻¹ month⁻¹ in December–February over the Southern Hemisphere, contributing to over 60% of the annual rainfall erosivity over large areas in each hemisphere. Rainfall erosivity was ~ 4 times higher during the most erosive months than the least erosive months (December–February and June–August in the Northern and Southern Hemisphere, respectively). The latitudinal distributions of monthly and seasonal rainfall erosivity were highly heterogeneous, with the tropics showing the greatest erosivity. The intra-annual variability of monthly rainfall erosivity was particularly high within 10–30° latitude in both hemispheres. The monthly rainfall erosivity maps can be used for improving spatiotemporal modeling of soil erosion and planning of soil conservation measures.

Abstract

Modeling monthly rainfall erosivity is vital to the optimization of measures to control soil erosion. Rain gauge data combined with satellite observations can aid in enhancing rainfall erosivity estimations. Here, we presented a framework which utilized Geographically Weighted Regression approach to model global monthly rainfall erosivity. The framework integrates long-term (2001–2020) mean annual rainfall erosivity estimates from IMERG (Global Precipitation Measurement (GPM) mission’s Integrated Multi-satellitE Retrievals for GPM) with station data from GloREDa (Global Rainfall Erosivity Database, n = 3,286 stations). The merged mean annual rainfall erosivity was disaggregated into mean monthly values based on monthly rainfall erosivity fractions derived from the original IMERG data. Global mean monthly rainfall erosivity was distinctly seasonal; erosivity peaked at ~ 200 MJ mm ha⁻¹ h⁻¹ month⁻¹ in June–August over the Northern Hemisphere and ~ 700 MJ mm ha⁻¹ h⁻¹ month⁻¹ in December–February over the Southern Hemisphere, contributing to over 60% of the annual rainfall erosivity over large areas in each hemisphere. Rainfall erosivity was ~ 4 times higher during the most erosive months than the least erosive months (December–February and June–August in the Northern and Southern Hemisphere, respectively). The latitudinal distributions of monthly and seasonal rainfall erosivity were highly heterogeneous, with the tropics showing the greatest erosivity. The intra-annual variability of monthly rainfall erosivity was particularly high within 10–30° latitude in both hemispheres. The monthly rainfall erosivity maps can be used for improving spatiotemporal modeling of soil erosion and planning of soil conservation measures.

Abstract

Climate models are vital for understanding and projecting global climate change and its associated impacts. However, these models suffer from biases that limit their accuracy in historical simulations and the trustworthiness of future projections. Addressing these challenges requires addressing internal variability, hindering the direct alignment between model simulations and observations, and thwarting conventional supervised learning methods. Here, we employ an unsupervised Cycle-consistent Generative Adversarial Network (CycleGAN), to correct daily Sea Surface Temperature (SST) simulations from the Community Earth System Model 2 (CESM2). Our results reveal that the CycleGAN not only corrects climatological biases but also improves the simulation of major dynamic modes including the El Niño-Southern Oscillation (ENSO) and the Indian Ocean Dipole mode, as well as SST extremes. Notably, it substantially corrects climatological SST biases, decreasing the globally averaged Root-Mean-Square Error (RMSE) by 58%. Intriguingly, the CycleGAN effectively addresses the well-known excessive westward bias in ENSO SST anomalies, a common issue in climate models that traditional methods, like quantile mapping, struggle to rectify. Additionally, it substantially improves the simulation of SST extremes, raising the pattern correlation coefficient (PCC) from 0.56 to 0.88 and lowering the RMSE from 0.5 to 0.32. This enhancement is attributed to better representations of interannual, intraseasonal, and synoptic scales variabilities. Our study offers a novel approach to correct global SST simulations and underscores its effectiveness across different time scales and primary dynamical modes.

Abstract

The actual application of Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG) is restricted by the bias revealed by ground data. This study established seven regression models (RMs) to generate the adjusted IMERG estimates. Relatively stable parameters of the regression can be gained during the calibration. The calibration was performed by climatological adjustment, building a relationship between the 18-year data series of the original IMERG estimates and simultaneous daily data derived from 687 rain gauges in mainland China. A one-time modeling scheme was designed using all daily precipitation data as a calibration dataset. A two-time modeling scheme was established by dividing the calibration period into cold and warm seasons. Then, the relative bias (RB) and root-mean-square error (RMSE) were the evaluation indicators inspected during the validation. Three rounds of modeling were designed to provide corrections when near-real-time and post-real-time IMERG products produce time series. The main conclusions are as follows: (1) The two-time modeling scheme had a higher proportion than the one-time modeling scheme in having the lowest RMSE and absolute RB values. (2) Compared with the original IMERG estimates, the model-generated estimates in rounds 1 and 2 reduced the magnitude of the RB and RMSE at around 75% and 85% of gauges, respectively. (3) Polynomial RMs were the best-performing models in rounds 2 and 3. (4) The gauges where the RM failed to reduce the magnitude of the RB were mainly found in humid, plain, and low-latitude areas of mainland China.

Abstract

Reliable drought prediction should be preceded to prevent damage from potential droughts. In this context, this study developed a hydrological drought prediction method, namely ensemble drought prediction (EDP) to reflect drought-related information under the ensemble streamflow prediction framework. After generating an ensemble of standardized runoff index by converting the ensemble of generated streamflow, the results were adopted as the prior distribution. Then, precipitation forecast and soil moisture were used to update the prior EDP. The EDP + A model included the precipitation forecast with the PDF-ratio method, and the observed soil moisture index was reflected in the former EDP and EDP + A via Bayes’ theorem, resulting in the EDP + S and EDP + AS models. Eight basins in Korea with more than 30 years of observation data were applied with the proposed methodology. As a result, the overall performance of the four EDP models yielded improved results than the climatological prediction. Moreover, reflecting soil moisture yielded improved evaluation metrics during short-term drought predictions, and in basins with larger drainage areas. Finally, the methodology presented in this study was more effective during periods with less intertemporal variabilities.

Abstract

Purpose of Review

Terrestrial ecosystems in the Arctic-Boreal region play a crucial role in the global carbon cycle as a carbon sink. However, rapid warming in this region induces uncertainties regarding the future net carbon exchange between land and the atmosphere, highlighting the need for better monitoring of the carbon fluxes. Solar-Induced chlorophyll Fluorescence (SIF), a good proxy for vegetation CO \(^{2}\) uptake, has been broadly utilized to assess vegetation dynamics and carbon uptake at the global scale. However, the full potential and limitations of SIF in the Arctic-Boreal region have not been explored. Therefore, this review aims to provide a comprehensive summary of the latest insights into Arctic-Boreal carbon uptake through SIF analyses, underscoring the advances and challenges of SIF in solving emergent unknowns in this region. Additionally, this review proposes applications of SIF across scales in support of other observational and modeling platforms for better understanding Arctic-Boreal vegetation dynamics and carbon fluxes.

Recent Findings

Cross-scale SIF measurements complement each other, offering valuable perspectives on Arctic-Boreal ecosystems, such as vegetation phenology, carbon uptake, carbon-water coupling, and ecosystem responses to disturbances. By incorporating SIF into land surface modeling, the understanding of Arctic-Boreal changes and their climate drivers can be mechanistically enhanced, providing critical insights into the changes of Arctic-Boreal ecosystems under global warming.

Summary

While SIF measurements are more abundant and with finer spatiotemporal resolutions, it is important to note that the coverage of these measurements is still limited and uneven in the Arctic-Boreal region. To address this limitation and further advance our understanding of the Arctic-Boreal carbon cycle, this review advocates for fostering a SIF network providing long-term and continuous measurements across spatial scales. Simultaneously measuring SIF and other environmental variables in the context of a multi-modal sensing system can help us comprehensively characterize Arctic-Boreal ecosystems with spatial details in land surface models, ultimately contributing to more robust climate projections.

Abstract

Monitoring and forecasting the spatiotemporal dynamics of vegetation across the Loess Plateau emerge as critical endeavors for environmental conservation, resource management, and strategic decision-making processes. Despite the swift advances in deep learning techniques for spatiotemporal prediction, their deployment for future vegetation forecasting remains underexplored. This investigation delves into vegetation alterations on the Loess Plateau from March 2000 to February 2023, employing fractional vegetation cover (FVC) as a metric, and scrutinizes its spatiotemporal interplay with precipitation and temperature. The introduction of a convolutional long short-term memory network enhanced by an attention mechanism (CBAM-ConvLSTM) aims to forecast vegetation dynamics on the Plateau over the ensuing 4 years, leveraging historical data on FVC, precipitation, and temperature. Findings revealed an ascending trajectory in the maximum annual FVC at a pace of 0.42% per annum, advancing from southeast to northwest, alongside a monthly average FVC increment at 0.02% per month. The principal driver behind FVC augmentation was identified as the growth season FVC surge in warm-temperate semi-arid and temperate semi-arid locales. Precipitation maintained a robust positive long-term association with FVC (Pearson coefficient > 0.7), whereas the temperature–FVC nexus displayed more variability, with periodic complementary trends. The CBAM-ConvLSTM framework, integrating FVC, precipitation, and temperature data, showcased commendable predictive accuracy. Future projections anticipate ongoing greening within the warm-temperate semi-arid region, contrasted by significant browning around the Loess Plateau’s peripheries. This research lays the groundwork for employing deep learning in the simulation of vegetation’s spatiotemporal dynamics.