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The volume of worldwide climate data is expanding rapidly, creating challenges for both physical archiving and sharing, as well as for ease of access and finding what's needed, particularly if you are not a climate scientist. The figure shows the projected increase in global climate data holdings for climate models, remotely sensed data, and in situ instrumental/proxy data.
Source publication
Climate data are dramatically increasing in volume and complexity, just as the users of these data in the scientific community
and the public are rapidly increasing in number. A new paradigm of more open, user-friendly data access is needed to ensure
that society can reduce vulnerability to climate variability and change, while at the same time exp...
Contexts in source publication
Context 1
... there has been an explosion in data from numerical climate model simulations, which have increased greatly in complexity and size. Data from these models are expected to become the largest and the fastest-growing segment of the global archive (Fig. 2). The archiving and sharing of output from climate models, particularly those run with a common experimental framework, began in the mid-1990s, starting with output from the early global coupled atmosphere-ocean general circulation models (AOGCMs) used for making future climate change projections (6). This led to the Coupled Model ...
Context 2
... variations as well as to anthropogenic climate change, and to guide the implementation of possible mitigation measures. This puts new demands on the variety, scale, and availability of observational data needed for model evaluation and development, and ex- pands, yet again, the volume of climate data that must be shared openly and efficiently (Fig. ...
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... Also, high-resolution climate models (such as those with 20-km grid spacing) are run for time slices, past and future, for integrations of a decade or two in order to obtain a better quantification of regional climate change and smaller-scale phenomena such as hurricanes [for example, see (15)]. The net result is a huge increase in data volume (Fig. 2). Early phases of the CMIP project involved less than 1 TB of model data, whereas CMIP3 archived 36 TB, and CMIP5 is expected to make avail- able 2.5 petabytes (PB). New capabilities of the Earth System Grid portal will provide distributed access to a large part of this new model output (16), making it possible for modeling groups to ...
Context 4
... Future of Climate Data: An Emerging Paradigm Thus, two major challenges for climate science revolve around data: ensuring that the ever- expanding volumes of data (Fig. 2) are easily and freely available to enable new scientific research, and making sure that these data and the results that depend on them are useful to and under- standable by a broad interdisciplinary audience. A new paradigm that joins traditional climate re- search with research on climate adaptation, ser- vices, assessment, and ...
Context 5
... increasingly daunting aspect of having tens and eventually hundreds of petabytes of climate data openly available for analysis (Fig. 2) is how to actually look at and use the data, all the while understanding uncertainties. More resources need to be dedicated to the development of so- phisticated software tools for sifting through, accessing, and visualizing the many model versions, experiments, and model fields (temperature, pre- cipitation, etc.), as well as all of ...
Citations
... The volume and complexity of climate information, including projections and data related to mitigation and adaptation, are rapidly increasing. This creates a paradox where more accessible information simultaneously becomes less comprehensible and digestible for the global population, which varies greatly in levels of education and access to information [12]. In this context, understanding weather and climate and effectively communicating scientific knowledge about climate change becomes crucial. ...
This study investigates the roles, perspectives, and communication strategies of National Meteorological Services (NMS) concerning climate monitoring, climate change projections, and the attribution of meteorological phenomena to climate variations. Using a survey distributed to 131 of the 193 National Meteorological Services affiliated with the World Meteorological Organization, we explore how these entities contribute to and communicate about the science of climate change. The survey targeted their involvement in observing and recording climate data, making climate projections, attributing specific weather events to climate change, and their methods of communicating these issues to the public and government officials. Responses were received from 44 countries, reflecting diverse levels of economic development and capabilities in handling, and disseminating complex climate information. The results show a strong engagement in traditional meteorological tasks, with a varied approach to the scientific study of climate change effects and public communication strategies. This study highlights the critical role of NMS in climate change research and underscores the challenges they face in effectively communicating complex climate information, which is crucial for public understanding and policymaking.
... When dealing with multiple samples, the typical analytical processes can be exceedingly costly and time-consuming (Poppiel et al., 2022). Tracking changes in the environment and agriculture has been made easier with the use of remote sensing in recent years (Jiménez-Lao et al., 2020;Li et al., 2020;Overpeck et al., 2011;Pavlovic et al., 2024). The system uses a variety of platforms and sensors, including satellite constellations and unmanned aerial systems (UAS), to collect data. ...
Introduction
Mapping soil organic carbon (SOC) with high precision is useful for controlling soil fertility and comprehending the global carbon cycle. Low-relief locations are characterized by minimal variability in traditional soil-forming elements, such as terrain and climatic conditions, which make it difficult to reflect the spatial variation of soil properties. In the meantime, vegetation cover makes it more difficult to obtain direct knowledge about agricultural soil. Crop growth and biomass are reflected by the normalized difference vegetation index (NDVI), a significant indicator. Rather than using conventional soil-forming variables.
Methods
In this study, a novel model for predicting SOC was developed using Landsat-8 Operational Land Imager (OLI) band data (Blue (B), Green (G), Red (R), and Near Infrared (NIR), NDVI data as the supporting variables, and Artificial Neural Networks (ANNs). A total of 120 surface soil samples were collected at a depth of 25 cm in the northeastern Nile Delta near Damietta City. Of these, 80% (96 samples) were randomly selected for model training, while the remaining 24 samples were used for testing and validation. Additionally, Gaussian Process Regression (GPR) models were trained to estimate SOC levels using the Matern 5/2 kernel within the Regression Learner framework.
Results and discussion
The results demonstrate that both the ANN with a multilayer feedforward network and the GPR model offer effective frameworks for SOC prediction. The ANN achieved an R² value of 0.84, while the GPR model with the Matern 5/2 kernel achieved a higher R² value of 0.89. These findings, supported by visual and statistical evaluations through cross-validation, confirm the reliability and accuracy of the models.
Conclusion
The systematic application of GPR within the Regression Learner framework provides a robust tool for SOC prediction, contributing to sustainable soil management and agricultural practices.
... The utilization of remote sensing technologies has emerged as an essential element in addressing multifaceted challenges pertinent to various disciplines concerning the observation and mitigation of the effects associated with climate change and global warming (Overpeck et al. 2011;Yang et al. 2013;Rasti et al. 2021). Nevertheless, the efficiency of remote sensing satellite sensors may be compromised by errors or artifacts, impacting the acquired imagery's precision and quality. ...
Long-term time series satellite imagery became highly essential for analyzing earth cycles such as global warming, climate change, and urbanization. Landsat‑7 satellite imagery plays a key role in this domain since it provides open-access data with expansive coverage and consistent temporal resolution for more than two decades. This paper addresses the challenge of stripe errors induced by Scan Line Corrector sensor malfunction in Landsat‑7 ETM+ satellite imagery, resulting in data loss and degradation. To overcome this problem, we propose a Generative Adversarial Networks approach to fill the gaps in the Landsat‑7 ETM+ panchromatic images. First, we introduce the YTU_STRIPE dataset, comprising Landsat‑8 OLI panchromatic images with synthetically induced stripe errors, for model training and testing. Our results indicate sufficient performance of the Pix2Pix GAN for this purpose. We demonstrate the efficiency of our approach through systematic experimentation and evaluation using various accuracy metrics, including Peak Signal-to-Noise Ratio, Structural Similarity Index Measurement, Universal Image Quality Index, Correlation Coefficient, and Root Mean Square Error which were calculated as 38.5570, 0.9206, 0.7670, 0.7753 and 3.8212, respectively. Our findings suggest promising prospects for utilizing synthetic imagery from Landsat‑8 OLI to mitigate stripe errors in Landsat‑7 ETM+ SLC-off imagery, thereby enhancing image reconstruction efforts. The datasets and model weights generated in this study are publicly available for further research and development: https://github.com/ynsemrevrl/eliminating-stripe-errors .
... It is worth considering that based on the precise distribution of CIPs along the Yangtze River, the risks of flood hazard they face in the context of future climate change should be of concern 40 . Especially in the flat terrain of the middle and lower reaches, where CIPs are highly concentrated and susceptible to direct attacks, the resulting hazards cannot be ignored 41 . ...
Strengthening industrial pollution control in the Yangtze River is a fundamental national policy of China. There is a lack of detailed distribution of chemical industrial parks (CIPs). This Study utilized random forest (RF) and active learning to generate the distribution map of CIPs along the Yangtze River at 10-m resolution. Based on Sentinel-2 imagery, spectral and texture features are extracted. Combined with the Points of Interest (POI), a multidimensional feature space is constructed. By employing partitioned training, classification of CIPs map is achieved on Google Earth Engine (GEE). Technical validation along the entire Yangtze River demonstrates a model accuracy of 80%. Compared to traditional manual survey methods, this approach saves significant time and economic costs while also being timelier. As the first publicly available CIPs map within a 5-km range along the Yangtze River, this research will provide a scientific basis for the fine governance of chemical industries in the region. Additionally, it offers a model guide for the accurate identification of the chemical industry.
... In order to assist human development, observational data and model simulations offer a fundamental understanding of a variety of hot problems, such as climate change, natural ecosystems, urbanization, and human health (Overpeck et al. 2011). However, remote sensing (RS) has steadily risen to prominence as a top study technique in social development studies because it provides multitemporal data and spatially comprehensive images (Yang et al. 2013). ...
Pakistan's geographic position and socioeconomic profile make it one of the nations that are particularly susceptible to the negative effects of climate change. The Tharparkar district in Pakistan is of particular importance in this regard as it is an arid region with serious environmental issues like drought, desertification, and soil degradation. Therefore, the purpose of this study is to examine how topographic and climatic factors affect vegetation indicators in the Tharparkar. The study utilizes spatiotemporal data spanning over 20 years (2001–2020) collected from the satellites MOD11A2 and MOD13A3. The collected data are processed using a range of tools in ArcGIS 10.4.1, and the impact of topographic and climatic conditions is analyzed based on different vegetation indices, including EVI, NDVI, STVI, OSAVI, and SAVI. The findings reveal that temperature and precipitation, both of which are controlled by topographic features, such as elevation and slope, are the key elements affecting vegetation in Tharparkar. At high elevations, rainfall (>440 mm) and LST (>39 °C) are also high and where the slope is low the density of vegetation indices is high.
... Climate data and reconstructions include station data, satellite data and paleo-climate data from tree rings, ice cores and lake and ocean sediment. These data are highly important as both a method of validating climate models and to better understand historical climate change 11 . For example, the Pliocene and Eocene epoch could be excellent analogies for some climate scenarios, providing a glimpse into what the future climate might look like 12 . ...
The accelerated warming of the planet caused by anthropogenic climate change is very concerning, as its impacts have the potential to be broad and its effects widespread. One climate change impact with significant interest from scientists, politicians, the media and the general public, is the proposed changes to infectious disease dynamics. Climate change has the potential to alter disease transmission through expansion to naive populations or by worsening risk factors. However, limitations exist in our ability to forecast the climate and disease, including how we incorporate changes in human behaviour and how we attribute climate/weather events solely to an infectious disease outcome. Broad statements about the impact of the climate on infectious disease may not be helpful, as these relationships are highly complex and likely lead to an oversimplification. The interdisciplinary field of climate-health research has the attention of those outside of science, and it is the responsibility of those involved to communicate attribution on an evidence basis, for better scientific communication and public spending. The uncertainty around the impacts of climate change is a call for action, to prevent pushing the Earth’s systems into the unknown.
... In situ, calibrated, and validated measurements of climate-sensitive observables are generally more informative about Earth system changes with longer data records [19][20][21] . However, this implies stationarity in the spatial and temporal distributions over which those measurements sample 22 . ...
In the 21st century, warmer temperatures and changing atmospheric circulation will likely produce unprecedented changes in Western United States snowfall1–3, with impacts on the timing, amount, and spatial patterns of snowpack4–7. The ~900 snow pillow stations are indispensable to water resource management by measuring snow-water equivalent (SWE)8,9 in strategic but fixed locations10,11. However, this network may not be impacted by climate change in the same way as the surrounding area¹² and thus fail to accurately represent unmeasured locations; climate change thereby threatens our ability to measure the effects of climate change on snow. In this work, we show that maintaining the current peak SWE estimation skill is nonetheless possible. We find that explicitly including spatial correlations—either from gridded observations or learned by the model—improves skill at predicting distributed snowpack from sparse observations by 184%. Existing artificial intelligence methods can be useful tools to harness the many available sources of snowpack information to estimate snowpack in a nonstationary climate.
... Unfortunately, the atmospheric circulation is a highly nonlinear system. With the increases in the types of observational data and model products, the disadvantage of conventional methods that are difficult to deal with data diversity has gradually appeared (Overpeck et al. 2011). Therefore, new methods should be explored to improve the forecast accuracy. ...
Predictions of daily maximum and minimum temperatures (Tmax and Tmin) are key components of operational weather forecasting. Here we show how a deep learning scheme can be used to improve their predictions based on the numerical weather prediction (NWP) output from the European Centre for Medium-Range Weather Forecasts − Integrated Forecasting System (ECMWF-IFS). Using an optimal factor set screened by a regression method, an error-correction model for Tmax and Tmin forecasts based on the Spatio-Temporal Stacked Residual Network (STS-ResNet) is established. We find that errors in Tmax and Tmin forecasts for Hunan Province, China, can be reduced by approximately 21% and 33% respectively. However, although the Tmax and Tmin forecasts at almost all terrain elevations have been improved, the improvement decreases with the increasing terrain elevation. To solve this problem, we designed the Residual and Spatial Attention STS-ResNet (SASTS-ResNet) based on spatial attention mechanism. In mountainous regions, the SASTS-ResNet makes up for the deficiency of STS-ResNet in improving the Tmax forecasts of the ECMWF-IFS (with the improvement increasing from 1.45 to 42.53%), which has also largely improved the Tmin forecasts (from 27 to 83%). Moreover, the ECMWF-IFS model, STS-ResNet and SASTS-ResNet all have some uncertainties in Tmax and Tmin in high-elevation areas, where the smallest uncertainty is found in the SASTS-ResNet model.
... Satellite remote sensing has been providing essential records of land surface and dynamics both synoptically and periodically for decades [1][2][3]. The long-term accumulations of remote sensing data have facilitated numerous applications in Earth system sciences and have enhanced our understanding of environmental changes [4,5]. ...
Remote sensing data are becoming increasingly important for quantifying long-term changes in land surfaces. Optical sensors onboard satellite platforms face a tradeoff between temporal and spatial resolutions. Spatiotemporal fusion models can produce high spatiotemporal data, while existing models are not designed to produce moderate-spatial-resolution data, like Moderate-Resolution Imaging Spectroradiometer (MODIS), which has moderate spatial detail and frequent temporal coverage. This limitation arises from the challenge of combining coarse- and fine-spatial-resolution data, due to their large spatial resolution gap. This study presents a novel model, named multi-scale convolutional neural network for spatiotemporal fusion (MSCSTF), to generate MODIS-like data by addressing the large spatial-scale gap in blending the Advanced Very-High-Resolution Radiometer (AVHRR) and Landsat images. To mitigate the considerable biases between AVHRR and Landsat with MODIS images, an image correction module is included into the model using deep supervision. The outcomes show that the modeled MODIS-like images are consistent with the observed ones in five tested areas, as evidenced by the root mean square errors (RMSE) of 0.030, 0.022, 0.075, 0.036, and 0.045, respectively. The model makes reasonable predictions on reconstructing retrospective MODIS-like data when evaluating against Landsat data. The proposed MSCSTF model outperforms six other comparative models in accuracy, with regional average RMSE values being lower by 0.005, 0.007, 0.073, 0.062, 0.070, and 0.060, respectively, compared to the counterparts in the other models. The developed method does not rely on MODIS images as input, and it has the potential to reconstruct MODIS-like data prior to 2000 for retrospective studies and applications.
... In recent years, remote sensing has emerged as a particularly effective method for tracking agricultural and environmental changes [23][24][25][26]. The technology relies on diverse sensors and platforms, such as satellite constellations and Unmanned Aerial Systems (UAS) to gather data, which are then typically processed using advanced algorithms, often in the realm of machine learning (ML) and deep learning (DL) [27]. ...
Monitoring soil organic carbon (SOC) typically assumes conducting a labor-intensive soil sampling campaign, followed by laboratory testing, which is both expensive and impractical for generating useful, spatially continuous data products. The present study leverages the power of machine learning (ML) and, in particular, deep neural networks (DNNs) for segmentation, as well as satellite imagery, to estimate the SOC remotely. We propose a new two-stage pipeline for remote SOC estimation, which relies on using a DNN trained to classify land cover to perform feature extraction, while the SOC estimation is performed by a different ML model. The first stage is an image segmentation DNN with the U-Net architecture, which is trained to estimate the land cover for an observed geographical region, based on multi-spectral images taken by the Sentinel-2 satellite constellation. This estimator is subsequently used to extract the latent feature vector for each of the output pixels, by rolling back from the output (dense) layer of the U-Net and accessing the last available convolutional layer of the same dimension as our desired output. The second stage is trained on a set of feature vectors extracted at the coordinates for which manual SOC measurements exist. We tested a variety of ML models and report on their performance. Using the best extremely randomized trees model, we generated a spatially continuous map of SOC estimations for the region of Tuscany, in Italy, with a resolution of 10 m, to share with the researchers as a means of validating the results and to demonstrate the efficiency of the proposed approach, which can can easily be scaled to create a global continuous SOC map.
