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Project: IPCC Data Distribution Centre : Supplementary data sets for the Sixth Assessment Report - For the Sixth Assessment Report of the IPCC (AR6) input/source and intermediate datasets underlying the AR6 were collected and long-term archived. This project compliments CMIP6 data subset and snapshot analyzed for the WGI AR6. Summary: This data set contains detailed elements the sea level projections associated with the Intergovernmental Panel on Climate Change Sixth Assessment Report. In particular, it contains relative sea level projections that exclude the background term (representing primarily land subsidence or uplift). It includes probability distributions for all the workflows described in AR6 WGI 9.6.3.2. P-boxes derived from these distributions are available in the sister entry 'IPCC-DDC_AR6_Sup_PBox'. These data may be of use for users who want to substitute their own estimates of the background term. Regional projections can also be accessed through the NASA/IPCC Sea Level Projections Tool at https://sealevel.nasa.gov/ipcc-ar6-sea-level-projection-tool. See https://zenodo.org/communities/ipcc-ar6-sea-level-projections for additional related data sets.

The expansion of irrigated agriculture has increased global crop production but resulted in widespread stress to freshwater resources. Ensuring that increases in irrigated production only occur in places where water is relatively abundant is a key objective of sustainable agriculture, and knowledge of how irrigated land has evolved is important for measuring progress towards water sustainability. Yet a spatially detailed understanding of the evolution of global area equipped for irrigation (AEI) is missing. Here we utilize the latest sub-national irrigation statistics (covering 17298 administrative units) from various official sources to develop a gridded (5 arc-min resolution) global product of AEI for the years 2000, 2005, 2010, and 2015. We find that AEI increased by 11% from 2000 (297 Mha) to 2015 (330 Mha) with locations of both substantial expansion (e.g., northwest India, northeast China) and decline (e.g., Russia). Combining these outputs with information on green (i.e., rainfall) and blue (i.e., surface and ground) water stress, we also examine to what extent irrigation has expanded unsustainably (i.e., in places already experiencing water stress). We find that more than half (52%) of irrigation expansion has taken place in regions that were already water stressed, with India alone accounting for 36% of global unsustainable expansion. These findings provide new insights into the evolving patterns of global irrigation with important implications for global water sustainability and food security. Recommended citation: Mehta, P., Siebert, S., Kummu, M. et al. Half of twenty-first century global irrigation expansion has been in water-stressed regions. Nat Water (2024). https://doi.org/10.1038/s44221-024-00206-9 Open-access peer reviewed publication available at https://www.nature.com/articles/s44221-024-00206-9 Files G_AEI_*.ASC were produced using the GMIA dataset[https://data.apps.fao.org/catalog/iso/f79213a0-88fd-11da-a88f-000d939bc5d8]. Files MEIER_G_AEI_*.ASC were produced using Meier et al. (2018) dataset [https://doi.pangaea.de/10.1594/PANGAEA.884744].

In alpine grasslands, increased N deposition is increasing the dominance of grasses relative to other functional types according to our previous study Shen et al. (2022). However, the mechanisms that drive this compositional change are not fully understood. We measured the effects of 4-6 years' N addition to simulate N deposition at rates of 0 (CK), 8 (N1), 24 (N2), 40 (N3), 56 (N4), and 72 (N5) kg N ha-1 year-1 on dominant representatives of four functional types, Leymus secalinus (grass), Carex capillifolia (sedge), Potentilla multifidi (non-leguminous forb), and Medicago ruthenica (legume), in the alpine grassland on the Qinghai-Tibetan Plateau (QTP). In-situ experiment showed that N addition increased aboveground biomass in L. secalinus but had negative or neutral effects on aboveground biomass in the other species. Consistent with this finding, N addition increased net photosynthesis, chlorophyll content, and rubisco activity in L. secalinus with less positive effects on the other species. Nitrogen addition increased leaf N content in L. secalinus and C. capillifolia and reduced leaf non-structural carbohydrate content in all four species. In L. secalinus, the highest N addition rate (N5) reduced MDA content, a marker of oxidative stress, by enhancing antioxidant enzyme activity. Overall, our findings suggested that physiological factors can contribute to increased competitiveness of grass relative to sedge, forb and legume species under high N application levels. The rapid growth of this grass species reduces resource availability to non-grass species, increasing its dominance in the alpine meadow.

AbstractA fine global future land use/land cover (LULC) is critical for demonstrating the geographic heterogeneity of earth system dynamics and human-earth interaction. In this study, we produced a 1 km global future LULC dataset that takes into account future climate and socio-economic changes as well as the impact of simulated results of the former year on temporally adjacent periods. By incorporating the variations in climatic and socio-economic factors, we differentiated LULC suitability probabilities for historical and future periods across representative SSP-RCP scenarios. Then, by using an improved cellular automata model-PLUS to simulate the patch-level changes of various land classes, we iteratively downscaled water-basin-level LULC demands in various future scenarios to a spatial resolution of 1 km. Our dataset achieves a high degree of simulation accuracy (Kappa = 0.94, OA = 0.97, FoM = 0.10) and precisely captures the spatial-temporal heterogeneity of global LULC changes under the combined effects of climate change and socio-economic development. This robust and fine-scale LULC dataset provides valuable spatially-explicit information essential for earth system modeling and intricate dynamics between anthropogenic activities and the environment.

The basic priority to neutralize carbon emissions (CE) is to achieve the sustainable development goal of climate action. In this regard, the role of renewable energy (RE) is widely debated. Transition to RE and environment-related innovation in technologies (ERIT) is a need of the hour. However, the challenge of uncertain economic policies in the transition process is interesting to study. Therefore, the present study intends to add a value to the literature by re-examining the interactive effect of economic policy uncertainty (EPU) in RE and ERIT and the transition process towards carbon neutrality. The second-generation econometric methodology is applied to empirically test the proposed interactive linkage of EPU, RE, ERIT, and CE. Findings reported the negative role of EPU in adopting RE and ERIT in seven emerging economies: Brazil, China, India, Indonesia, Mexico, Russia, and Turkey. However, the ERIT and RE are found to be supportive of neutralizing the CE. Emerging seven (E7) countries are suggested to be consistent in their economic policies to nurture the transition process toward a sustainable environment.

Abstract The vegetation optical depth (VOD), a vegetation index retrieved from passive or active microwave remote sensing systems, is related to the intensity of microwave extinction effects within the vegetation canopy layer. This index is only marginally impacted by effects from atmosphere, clouds and sun illumination, and thus increasingly used for ecological applications at large scales. Newly released VOD products show different abilities in monitoring vegetation features, depending on the algorithm used and the satellite frequency. VOD is increasingly sensitive to the upper vegetation layer as the frequency increases (from L-, C- to X-band), offering different capacities to monitor seasonal changes of the leafy and/or woody vegetation components, vegetation water status and aboveground biomass. This study evaluated nine recently developed/reprocessed VOD products from the AMSR2, SMOS and SMAP space-borne instruments for monitoring structural vegetation features related to phenology, height and aboveground biomass. For monitoring the seasonality of green vegetation (herbaceous and woody foliage), we found that X-VOD products, particularly from the LPDR-retrieval algorithm, outperformed the other VOD products in regions that are not densely vegetated, where they showed higher temporal correlation values with optical vegetation indices (VIs). However, LPDR X-VOD time series failed to detect changes in VOD after rainfall events whereas most other VOD products could do so, and overall daily variations are less pronounced in LPDR X-VOD. Results show that the reprocessed VODCA C- and X-VOD have almost comparable performance and VODCA C-VOD correlates better with VIs than other C-VOD products. Low frequency L-VOD, particularly the new version (V2) of SMOS-IC, show a higher temporal correlation with VIs, similar to C-VOD, in medium-densely vegetated biomes such as savannas (R ~ 0.70) than for other short vegetation types. Because the L-VOD indices are more sensitive to the non-green vegetation components (trunks and branches) than higher frequency products, they are well-correlated with aboveground biomass: (R ~ 0.91) across space between predicted and observed values for both SMOS-IC V2 and SMAP MT-DCA. However, when compared with forest canopy height, results at L-band are not systematically better than C- and X-VOD products. This revealed specific VOD retrieval issues for some ecosystems, e.g., boreal regions. It is expected that these findings can contribute to algorithm refinements, product enhancements and further developing the use of VOD for monitoring above-ground vegetation biomass, vegetation dynamics and phenology.

Drought is the most serious natural disaster causing severe damage to agriculture. Drought impacts on rice (Oryza sativa) production present a major threat to future global food security. In this paper, the Environmental Policy Integrated Climate (EPIC) model was used to simulate the growth of rice, in different periods (short-term (2019-2039), medium-term (2040-2069), long-term (2070-2099)), based on multiple Representative Concentration Pathways (RCP) scenarios. Drought intensity and rice physical vulnerability curves were assessed, based on the output parameters of EPIC, to evaluate global rice yield risk, due to drought. The results show that the average expected loss rate of global rice yield may reach 13.1% (±0.4%) in the future. The high-risk area of rice drought is mainly located in the north of 30°N. The fluctuation of rice drought risk and the proportion of increased risk areas will increase significantly. About 77.6% of the changes in rice drought risk are explained by variations in shortwave radiation (r = 0.88). Projections show that the average value of daily shortwave radiation increases by 1 W/m2 during the rice growth period, accompanied by an expected rice yield loss rate of about 12.7%. The rice drought risk methods presented in this paper provide plausible estimates of forecasting future drought risk under climate change, and address challenges of sparse data; we believe these methods can be applied to decisions for reducing drought-related crop losses and ensuring global food security.