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GeoNEX is a collaborative project led by scientists from NASA, NOAA, and many other institutes around the world to generate Earth monitoring products using data streams from the latest Geostationary (GEO) sensors including the GOES-16/17 Advanced Baseline Imager (ABI), the Himawari-8/9 Advanced Himawari Imager (AHI), and more. An accurate and consistent product of the Top-Of-Atmosphere (TOA) reflectance and brightness temperature is the starting point in the scientific processing pipeline and has significant influences on the downstream products. This paper describes the main steps and the algorithms in generating the GeoNEX TOA products, starting from the conversion of digital numbers to physical quantities with the latest radiometric calibration information. We implement algorithms to detect and remove residual georegistration uncertainties automatically in both GOES and Himawari L1bdata, adjust the data for topographic relief, estimate the pixelwise data-acquisition time, and accurately calculate the solar illumination angles for each pixel in the domain at every time step. Finally, we reproject the TOA products to a globally tiled common grid in geographic coordinates in order to facilitate intercomparisons and/or synergies between the GeoNEX products and existing Earth observation datasets from polar-orbiting satellites.

Long-term global data sets of vegetation Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation absorbed by vegetation (FPAR) are critical to monitoring global vegetation dynamics and for modeling exchanges of energy, mass and momentum between the land surface and planetary boundary layer. LAI and FPAR are also state variables in hydrological, ecological, biogeochemical and crop-yield models. The generation, evaluation and an example case study documenting the utility of 30-year long data sets of LAI and FPAR are described in this article. A neural network algorithm was first developed between the new improved third generation Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) and best-quality Terra Moderate Resolution Imaging Spectroradiometer (MODIS) LAI and FPAR products for the overlapping period 2000–2009. The trained neural network algorithm was then used to generate corresponding LAI3g and FPAR3g data sets with the following attributes: 15-day temporal frequency, 1/12 degree spatial resolution and temporal span of July 1981 to December 2011. The quality of these data sets for scientific research in other disciplines was assessed through (a) comparisons with field measurements scaled to the spatial resolution of the data products, (b) comparisons with broadly-used existing alternate satellite data-based products, (c) comparisons to plant growth limiting climatic variables in the northern latitudes and tropical regions, and (d) correlations of dominant modes of interannual variability with large-scale circulation anomalies such as the EI Niño-Southern Oscillation and Arctic Oscillation. These assessment efforts yielded results that attested to the suitability of these data sets for research use in other disciplines. The utility of these data sets is documented by comparing the seasonal profiles of LAI3g with profiles from 18 state-of-the-art Earth System Models: the models consistently overestimated the satellite-based estimates of leaf area and simulated delayed peak seasonal values in the northern latitudes, a result that is consistent with previous evaluations of similar models with ground-based data. The LAI3g and FPAR3g data sets can be obtained freely from the NASA Earth Exchange (NEX) website.

最近,NASA发布了一套基于CMIP5 21个耦合模式输出的高分辨率降尺度逐日数据集,简称NEX-GDDP。本文评估了NEX-GDDP对中国极端降水的模拟性能。研究发现:(1)相比CMIP5直接输出结果,NEX-GDDP能够更好刻画中国极端降水的空间分布;(2)未来中国极端降水事件明显增多、强度增强,NEX-GDDP在区域尺度上给出了更多的气候变化信息;(3)NEXGDDP预估的中国未来极端降水变化的不确定性范围相比CMIP5直接输出结果明显减少,使得预估结果更加可靠.

Antarctica directly impacts the lives of more than half of the world’s population living in the coastal regions. Therefore it is highly desirable to project its climate for the future. But it is a region where the climate models have large inter-modal variability and hence it raises questions about the robustness of the projections available. Therefore, we have examined 87 global models from three modelling consortia (Coupled Model Intercomparison Project Phase 5 (CMIP5), Coupled Model Intercomparison Project Phase 6 (CMIP6), and NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP)), characterized their fidelity, selected a set of ten models (MM10) performing satisfactorily, and used them to make the future projection of precipitation and temperature, and assessed the contribution of precipitation towards sea-levels. For the historical period, the multi-model mean (MMM) of CMIP5 performed slightly better than CMIP6 and was worse for NEX-GDDP, with negligible surface temperature bias of approximately 0.5 °C and a 17.5% and 19% biases in the mean precipitation noted in both CMIP consortia. These biases considerably reduced in MM10, with 21st century projections showing surface warming of approximately 5.1 °C–5.3 °C and precipitation increase approximately 44%–50% against ERA-5 under high-emission scenarios in both CMIP consortia. This projected precipitation increase is much less than that projected using MMM in previous studies with almost the same level of warming, implying approximately 40.0 mm yr −1 contribution of precipitation towards sea-level mitigation against approximately 65.0 mm yr −1 .

This paper evaluates the NASA Earth Exchange Global Daily Downscaled Projections’ (NEX-GDDP) CMIP6 models’ performance in simulating extreme climate indices across China and its eight subregions for the period 2081–2100 under SSP1-2.6, SSP2-4.5 and SSP5-8.5 scenarios. The models effectively reproduce the spatial patterns of extreme high temperatures, especially in northern China. They show enhanced capabilities in accurately simulating the maximum daily maximum temperature (TXx) and the number of high temperature days (T35). They improve the cold bias of the TXx index in Northwest China and warm bias in South China. In terms of precipitation, the models demonstrate strong performance, evidenced by significant spatial correlations in total wet day precipitation (PTOT) simulations. They reduce the biases of PTOT and simple daily intensity (SDII) compared to CMIP6 models. Regionally, they enhance PTOT accuracy along southern coasts and in Yunnan, better captures very heavy precipitation days (R20) in the Southwest region, max 5-day precipitation (RX5D) in North China and Southwest region, and SDII in the Northeast region and Yunnan. Under SSP5-8.5 scenario, significant impacts include increased TXx in Northwest China, more heatwave days in Southwest China, and more T35 in South China. Extreme precipitation will become more frequent in South and East China, with the greatest intensity increases in Southwest China (SWC1). North China will see fewest consecutive dry days (CDD) indices, while consecutive wet days (CWD) will prominently rise in SWC1.

The increase in record‐breaking extreme events caused by climate change poses a threat to human health and well‐being. Understanding the future impacts of such events on global populations can provide decision‐making support for policies aiming to mitigate climate change. Here, we investigated the population exposure to eight climate extreme indices and drivers of exposure trajectories based on National Aeronautics and Space Administration Earth Exchange Global Daily Downscaled Projections Coupled Model Intercomparison Project 6 and population projection data under four shared socioeconomic pathway scenarios at a spatial resolution of 0.25° × 0.25°. The results show that by the mid‐twenty‐first century, most regions worldwide, especially Africa and South America, will continue to experience record‐breaking temperatures and compound drought and heatwaves (CDHWs). Regarding population exposure, under SSP3‐7.0 in the late twenty‐first century, the mean value of the multimodel median expected annual exposure (EAE) of all extreme temperature indices and CDHW reaches 8.12 billion persons per year. Population exposure hotspots will be concentrated in Central Africa, South Asia, Southeast Asia, and East Asia, mostly in developing countries, where 55.01%–87.42% of the EAE is found. The drivers of exposure trajectories are spatially heterogeneous. The increase in record‐breaking probability contributes more than population growth to EAE growth in most regions of the world except Central Asia, the Middle East, and most of Africa. These findings reveal the future trajectories of record‐breaking probabilities and population exposures for climate extremes, which can inform understanding of the intersections between climate change and population change and future risk management. Plain Language Summary Climate change causes rapid increases in extreme weather events that threaten human health and well‐being. Understanding the future impacts of climate change on the global population can inform policies aiming to mitigate climate change. Here, we investigated the spatiotemporal dynamics of future record‐breaking extreme temperature and precipitation events, sequential precipitation and heatwaves (hot extremes after heavy precipitation), and compound drought and heatwaves (cooccurring dry and hot extremes) (CDHWs) and analyzed how populations may be potentially affected by these events based on the latest available climate model data and future population projections. The results show that by the mid‐twenty‐first century, most regions worldwide, especially Africa and South America, will continue to experience record‐breaking temperatures and CDHWs. Regions where populations will be most affected include Central Africa, South Asia, Southeast Asia, and East Asia, mostly developing countries. This increase in the affected population is due to the growth of the population and the increase in record‐breaking extreme events; record‐breaking extreme event increases contribute more than population growth in most regions of the world except Central Asia, the Middle East, and most of Africa. Overall, this study projected the spatiotemporal patterns of unprecedented climate extremes and affected populations to inform risk management. Key Points Africa and South America will experience successive record‐breaking extreme events and even compound droughts and heatwaves Population exposure will be highly uneven and largely concentrated in underdeveloped areas A record‐breaking increase in probability will be the major driver of the increase in population exposure in most regions of the world

A new bias-corrected, statistically downscaled product, the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset, has been developed and released to help in understanding climate change at local to regional scales. Here, we evaluate the performance of the NEX-GDDP data in simulating daily maximum temperature (TX) and daily minimum temperature (TN) in the historical period 1961–2005 over China at national and regional scales. Projected future changes in TX and TN are assessed under the Representative Concentration Pathways (RCPs) 4.5 and 8.5 emissions scenarios. Results show that the NEX-GDDP data can capture the basic spatial patterns of TX and TN, but these results underestimate the warming trends of TX and TN from 1961 to 2005 over China. The largest biases are found in western China due to its complex terrain conditions; these biases are 2.33 and 2.21 times larger than those found in eastern China for TX and TN, respectively. The climate projections show that the difference in uncertainties is small between the east and the west, and higher warming changes correspond to greater uncertainties. The increasing trends under the RCP8.5 are 2.22 and 2.31 times the size found under the RCP4.5 by the end of the twenty-first century for TX and TN, respectively. The Tibetan plateau has the fastest warming trend under the two scenarios.

We identified a set of bias-corrected and downscaled Coupled Model Intercomparison Project 6 (CMIP6) models capable of accurately simulating the observed mean Indian summer monsoon rainfall, extreme rain events (EREs) and their respective interannual variability. The future changes in EREs projected by these models for four climate change scenarios—Shared Socioeconomic Pathways (SSPs), 1–2.6, 2–4.5, 3–7.0 and 5–8.5 were estimated using percentile thresholds. Under the highest emission scenario, SSP5-8.5, at the end of the century, summer monsoon season total rainfall exhibits a 1.1-fold increase, while extreme rainfall intensity demonstrates a more substantial rise of 1.3-fold. The spatial variability of seasonal total rainfall increases by 1.2-fold compared to the baseline period, with an even more pronounced 2.1-fold increase in the spatial variability of extreme rainfall (R99p). These findings underscore the significant amplification of rainfall variability and intensity under the most extreme climate scenario. The intensity and frequency of very extreme rainfall events (vEREs) were also found to increase, though with a substantial inter-model spread. Under SSP5-8.5, extreme rainfall intensity scales with temperature at 1.5 to 2 times the Clausius-Clapeyron (CC) rate. While mid-century scenarios show minimal variations in extreme rainfall intensity from the historical period, end-century projections reveal significant shifts; an increase in north India and a decrease in south India due to cloud-induced cooling effects.

Global climate models (GCMs) are the state-of-the-art tool for understanding climate change and predicting future. However, little research has been reported on the latest NEX-GDDP-CMIP6 product in China. The purpose of this study was to evaluate the simulated performance and drought capture utility of the NEX-GDDP-CMIP6 over China. First, the simulation skills of the 16 GCMs in NEX-GDDP-CMIP6 was evaluated by the 'DISO', a big data evaluation method. Second, the DISO framework for drought identification was constructed by coupling the Correlation Coefficient (CC), False Alarm Rate (FAR) and Probability of Detection (POD). Then, it was combined with SPI and SPEI to evaluate the drought detection capability of NEX-GDPD-CMIP6. The result shows that: (1) NEX-GDPD-CMIP6 can reproduce the spatial distribution pattern of historical precipitation and temperature, which performs well in simulating warming trend but fails to capture precipitation's fluctuation characteristics. (2) The best performing model in precipitation is ACCESS-CM2 (DISO 1.630) and in temperature is CESM2 (DISO 3.246). (3) The 16MME performs better than the best single model, indicating that multi-model ensemble can effectively reduce the uncertainty inherent in models. (4) The SPEI calculated by 16MME identifying drought well in arid, while SPI is recommended for other climate classifications of China.

The Paris Agreement and the Special Report on Global Warming of 1.5 °C from the Intergovernmental Panel on Climate Change (IPCC) highlighted the potential risks of climate change across different global warming levels (GWLs). The increasing occurrence of extreme high-temperature events is linked to a warmer climate that is particularly prevalent in the Arabian Peninsula (AP). This study investigates future changes in temperatures and related extremes over AP, under four GWLs, such as 1.5 °C, 2.0 °C, 3.0 °C, and 4.0 °C, with three different Shared Socioeconomic Pathways (SSPs: SSP1-2.6, SSP2-4.5, and SSP5-8.5). The study uses high-resolution datasets of 27 models from the NASA Earth Exchange Global Daily Downscaled Projections of the Coupled Model Intercomparison Project Phase 6 (NEX-GDDP-CMIP6). The results showed that the NEX-GDDP-CMIP6 individual models and their multi-model means reasonably captured the extreme temperature events. The summer maximum and winter minimum temperatures are projected to increase by 0.11–0.67 °C and 0.09–0.70 °C per decade under the selected SSPs. Likewise, the projected temperature extremes exhibit significant warming with varying degrees across the GWLs under the selected SSPs. The warm temperature extremes are projected to increase, while the cold extremes are projected to decrease under all GWLs and the selected SSPs. Overall, the findings provide a comprehensive assessment of temperature changes over AP in response to global warming, which can be helpful in the development of climate adaptation and mitigation strategies.