Towards Constituting Mathematical Structures for Learning to Optimize

Learning to Optimize (L2O), a technique that utilizes machine learning to learn an optimization algorithm automatically from data, has gained arising attention in recent years. A generic L2O approach parameterizes the iterative update rule and learns the update direction as a black-box network. While the generic approach is widely applicable, the learned model can overfit and may not generalize well to out-of-distribution test sets. In this paper, we derive the basic mathematical conditions that successful update rules commonly satisfy. Consequently, we propose a novel L2O model with a mathematics-inspired structure that is broadly applicable and generalized well to out-of-distribution problems. Numerical simulations validate our theoretical findings and demonstrate the superior empirical performance of the proposed L2O model.Comment: ICML 202

Hydroclimatic extremes contribute to asymmetric trends in ecosystem productivity loss

Gross primary production is the basis of global carbon uptake. Gross primary production losses are often related to hydroclimatic extremes such as droughts and heatwaves, but the trend of such losses driven by hydroclimatic extremes remains unclear. Using observationally-constrained and process-based model data from 1982-2016, we show that drought-heat events, drought-cold events, droughts and heatwaves are the dominant drivers of gross primary production loss. Losses associated with these drivers increase in northern midlatitude ecosystem but decrease in pantropical ecosystems, thereby contributing to around 70% of the variability in total gross primary production losses. These asymmetric trends are caused by an increase in the magnitude of gross primary production losses in northern midlatitudes and by a decrease in the frequency of gross primary production loss events in pantropical ecosystems. Our results suggest that the pantropics may have become less vulnerable to hydroclimatic variability over recent decades whereas gross primary production losses and hydroclimatic extremes in northern midlatitudes have become more closely entangled

The paleoclimatic footprint in the soil carbon stock of the Tibetan permafrost region

Data and code availability The authors declare that the majority of the data supporting the findings of this study are available through the links given in the paper. The unpublished data are available from the corresponding author upon request. The new estimate of Tibetan soil carbon stock and R code are available in a persistent repository (https://figshare.com/s/4374f28d880f366eff6d). Acknowledgements This study was supported by the Strategic Priority Research Program (A) of the Chinese Academy of Sciences (XDA20050101), the National Natural Science Foundation of China (41871104), Key Research and Development Programs for Global Change and Adaptation (2017YFA0603604), International Partnership Program of the Chinese Academy of Sciences (131C11KYSB20160061) and the Thousand Youth Talents Plan project in China. Jinzhi Ding acknowledges the General (2017M620922) and the Special Grade (2018T110144) of the Financial Grant from the China Postdoctoral Science Foundation.Peer reviewedPublisher PD

Conservation tillage increases corn and soybean water productivity across the Ohio River Basin

Optimizing agricultural management practices is imperative for ensuring food security and building climate-resilient agriculture. The past several decades have witnessed the emergence of conservation tillage practices to combat soil erosion and degradation. However, the effects of conservation tillage on crop water productivity (CWP) remain uncertain, especially from a regional-scale perspective. Here, we used an improved process-based agroecosystem model (DLEM-Ag) to quantify the long-term effects of conservation tillage (e.g., no-tillage, NT; reduced tillage, RT) on CWP (defined as the ratio of crop productivity to evapotranspiration) of corn and soybean across the Ohio River Basin during 1979–2018. Our results revealed an average increase of 2.8% and 8.4% in CWP for corn and soybean, respectively, under the NT adoption scenario. Compared to the conventional tillage scenario, NT and RT would enhance CWP, primarily due to reductions in evapotranspiration, particularly evaporation. Further analysis suggested that, although NT and RT may decrease surface runoff, these practices could also increase subsurface drainage and nutrient loss from corn and soybean farmland via leaching. These results indicate that conservation tillage should be complemented with additional water and nutrient management practices to enhance soil water retention and optimize nutrient use in the region's cropland. Our findings also provide unique insights into optimizing management practices for other areas where conservation tillage is widely applied

Forty years of reform and opening up:China’s progress toward a sustainable path

After 40 years of reform and “opening up,” China has made remarkable economic progress. Such economic prosperity, however, has been coupled with environmental degradation. We analyze diverse long-term data to determine whether China is experiencing a decoupling of economic growth and environmental impacts, and where China stands with respect to the Sustainable Development Goals (SDGs) in terms of reducing regional division, urban-rural gap, social inequality, and land-based impacts on oceans. The results highlight that China’s desire to achieve “ecological civilization” has resulted in a decoupling trend for major pollutants since 2015, while strong coupling remains with CO2 emissions. Progress has been made in health care provision, poverty reduction, and gender equity in education, while income disparity continues between regions and with rural-urban populations. There is a considerable way to go toward achieving delivery of the SDGs; however, China’s progress toward economic prosperity and concomitant sustainability provides important insights for other countries

Benchmarking carbon fluxes of the ISIMIP2a biome models

The purpose of this study is to evaluate the eight ISIMIP2a biome models against independent estimates of long-term net carbon fluxes (i.e. Net Biome Productivity, NBP) over terrestrial ecosystems for the recent four decades (1971–2010). We evaluate modeled global NBP against 1) the updated global residual land sink (RLS) plus land use emissions (E LUC) from the Global Carbon Project (GCP), presented as R + L in this study by Le Quéré et al (2015), and 2) the land CO2 fluxes from two atmospheric inversion systems: Jena CarboScope s81_v3.8 and CAMS v15r2, referred to as F Jena and F CAMS respectively. The model ensemble-mean NBP (that includes seven models with land-use change) is higher than but within the uncertainty of R + L, while the simulated positive NBP trend over the last 30 yr is lower than that from R + L and from the two inversion systems. ISIMIP2a biome models well capture the interannual variation of global net terrestrial ecosystem carbon fluxes. Tropical NBP represents 31 ± 17% of global total NBP during the past decades, and the year-to-year variation of tropical NBP contributes most of the interannual variation of global NBP. According to the models, increasing Net Primary Productivity (NPP) was the main cause for the generally increasing NBP. Significant global NBP anomalies from the long-term mean between the two phases of El Niño Southern Oscillation (ENSO) events are simulated by all models (p < 0.05), which is consistent with the R + L estimate (p = 0.06), also mainly attributed to NPP anomalies, rather than to changes in heterotrophic respiration (Rh). The global NPP and NBP anomalies during ENSO events are dominated by their anomalies in tropical regions impacted by tropical climate variability. Multiple regressions between R + L, F Jena and F CAMS interannual variations and tropical climate variations reveal a significant negative response of global net terrestrial ecosystem carbon fluxes to tropical mean annual temperature variation, and a non-significant response to tropical annual precipitation variation. According to the models, tropical precipitation is a more important driver, suggesting that some models do not capture the roles of precipitation and temperature changes adequately

Interaction between pollution and climate change augments ecological risk to a coastal ecosystem

Pollution and climate change are among the most challenging issues for countries with developing economies, but we know little about the ecological risks that result when these pressures occur together. We explored direct effects of, and interactions between, environmental pollution and climate change on ecosystem health in the Bohai Sea region of Northern China. We developed an integrated approach to assess ecological risks to this region under four scenarios of climate change. Although ecological risks to the system from pollution alone have been declining, interactions between pollution and climate change have enhanced ecological risks to this coastal/marine ecosystem. Our results suggest that current policies focused strictly on pollution control alone should be changed to take into account the interactive effects of climate change so as to better forecast and manage potential ecological risks