UPIR: Toward the Design of Unified Parallel Intermediate Representation for Parallel Programming Models

The complexity of heterogeneous computing architectures, as well as the demand for productive and portable parallel application development, have driven the evolution of parallel programming models to become more comprehensive and complex than before. Enhancing the conventional compilation technologies and software infrastructure to be parallelism-aware has become one of the main goals of recent compiler development. In this paper, we propose the design of unified parallel intermediate representation (UPIR) for multiple parallel programming models and for enabling unified compiler transformation for the models. UPIR specifies three commonly used parallelism patterns (SPMD, data and task parallelism), data attributes and explicit data movement and memory management, and synchronization operations used in parallel programming. We demonstrate UPIR via a prototype implementation in the ROSE compiler for unifying IR for both OpenMP and OpenACC and in both C/C++ and Fortran, for unifying the transformation that lowers both OpenMP and OpenACC code to LLVM runtime, and for exporting UPIR to LLVM MLIR dialect.Comment: Typos corrected. Format update

Spring Hydrology Determines Summer Net Carbon Uptake in Northern Ecosystems

Increased photosynthetic activity and enhanced seasonal CO2 exchange of northern ecosystems have been observed from a variety of sources including satellite vegetation indices (such as the Normalized Difference Vegetation Index; NDVI) and atmospheric CO2 measurements. Most of these changes have been attributed to strong warming trends in the northern high latitudes (greater than or equal to 50N). Here we analyze the interannual variation of summer net carbon uptake derived from atmospheric CO2 measurements and satellite NDVI in relation to surface meteorology from regional observational records. We find that increases in spring precipitation and snow pack promote summer net carbon uptake of northern ecosystems independent of air temperature effects. However, satellite NDVI measurements still show an overall benefit of summer photosynthetic activity from regional warming and limited impact of spring precipitation. This discrepancy is attributed to a similar response of photosynthesis and respiration to warming and thus reduced sensitivity of net ecosystem carbon uptake to temperature. Further analysis of boreal tower eddy covariance CO2 flux measurements indicates that summer net carbon uptake is positively correlated with early growing-season surface soil moisture, which is also strongly affected by spring precipitation and snow pack based on analysis of satellite soil moisture retrievals. This is attributed to strong regulation of spring hydrology on soil respiration in relatively wet boreal and arctic ecosystems. These results document the important role of spring hydrology in determining summer net carbon uptake and contrast with prevailing assumptions of dominant cold temperature limitations to high-latitude ecosystems. Our results indicate potentially stronger coupling of boreal/arctic water and carbon cycles with continued regional warming trends

Spring hydrology determines summer net carbon uptake in northern ecosystems

Increased photosynthetic activity and enhanced seasonal CO2 exchange of northern ecosystems have been observed from a variety of sources including satellite vegetation indices (such as the normalized difference vegetation index; NDVI) and atmospheric CO2 measurements. Most of these changes have been attributed to strong warming trends in the northern high latitudes (50° N). Here we analyze the interannual variation of summer net carbon uptake derived from atmospheric CO2 measurements and satellite NDVI in relation to surface meteorology from regional observational records. We find that increases in spring precipitation and snow pack promote summer net carbon uptake of northern ecosystems independent of air temperature effects. However, satellite NDVI measurements still show an overall benefit of summer photosynthetic activity from regional warming and limited impact of spring precipitation. This discrepancy is attributed to a similar response of photosynthesis and respiration to warming and thus reduced sensitivity of net ecosystem carbon uptake to temperature. Further analysis of boreal tower eddy covariance CO2 flux measurements indicates that summer net carbon uptake is positively correlated with early growing-season surface soil moisture, which is also strongly affected by spring precipitation and snow pack based on analysis of satellite soil moisture retrievals. This is attributed to strong regulation of spring hydrology on soil respiration in relatively wet boreal and arctic ecosystems. These results document the important role of spring hydrology in determining summer net carbon uptake and contrast with prevailing assumptions of dominant cold temperature limitations to high-latitude ecosystems. Our results indicate potentially stronger coupling of boreal/arctic water and carbon cycles with continued regional warming trends

Hydrological Response of Alpine Wetlands to ClimateWarming in the Eastern Tibetan Plateau

Alpine wetlands in the Tibetan Plateau (TP) play a crucial role in the regional hydrological cycle due to their strong influence on surface ecohydrological processes; therefore, understanding how TP wetlands respond to climate change is essential for projecting their future condition and potential vulnerability. We investigated the hydrological responses of a large TP wetland complex to recent climate change, by combining multiple satellite observations and in-situ hydro-meteorological records. We found different responses of runoff production to regional warming trends among three basins with similar climate, topography and vegetation cover but different wetland proportions. The basin with larger wetland proportion (40.1%) had a lower mean runoff coefficient (0.173 ± 0.006), and also showed increasingly lower runoff level (−3.9% year−1, p = 0.002) than the two adjacent basins. The satellite-based observations showed an increasing trend of annual non-frozen period, especially in the wetland-dominated region (2.64 day·year−1, p \u3c 0.10), and a strong extension of vegetation growing-season (0.26–0.41 day·year−1, p \u3c 0.10). Relatively strong increasing trends in evapotranspiration (ET) (~1.00 mm·year−1, p \u3c 0.01) and the vertical temperature gradient above ground surface (0.043 °C·year−1, p \u3c 0.05) in wetland-dominant areas were documented from satellite-based ET observations and weather station records. These results indicate recent surface drying and runoff reduction of alpine wetlands, and their potential vulnerability to degradation with continued climate warming

Climatic Controls on Spring Onset of the Tibetan Plateau Grasslands from 1982 to 2008

Understanding environmental controls on vegetation spring onset (SO) in the Tibetan Plateau (TP) is crucial to diagnosing regional ecosystem responses to climate change. We investigated environmental controls on the SO of the TP grasslands using satellite vegetation index (VI) from the 3rd Global Inventory Modeling and Mapping Studies (GIMMS3g) product, with in situ air temperature (Ta) and precipitation (Prcp) measurement records from 1982 to 2008. The SO was determined using a dynamic threshold method based on a 25% threshold of seasonal VI amplitude. We find that SO shows overall close associations with spring Ta, but is also subject to regulation from spring precipitation. In relatively dry but increasingly wetting (0.50 mm·year−1, p \u3c 0.10) grasslands (mean spring Prcp = 22.8 mm; Ta = −3.27 °C), more precipitation tends to advance SO (−0.146 day·mm−1, p = 0.150) before the mid-1990s, but delays SO (0.110 day·mm−1, p = 0.108) over the latter record attributed to lower solar radiation and cooler temperatures associated with Prcp increases in recent years. In contrast, in relatively humid TP grasslands (73.0 mm; −3.51 °C), more precipitation delays SO (0.036 day·mm−1, p = 0.165) despite regional warming (0.045 °C·year−1, p \u3c 0.05); the SO also shows a delaying response to a standardized drought index (mean R = 0.266), indicating a low energy constraint to vegetation onset. Our results highlight the importance of surface moisture status in regulating the phenological response of alpine grasslands to climate warming

Sensitivity of active-layer freezing process to snow cover in Arctic Alaska

The contribution of cold-season soil respiration to the Arctic–boreal carbon cycle and its potential feedback to the global climate remain poorly quantified, partly due to a poor understanding of changes in the soil thermal regime and liquid water content during the soil-freezing process. Here, we characterized the processes controlling active-layer freezing in Arctic Alaska using an integrated approach combining in situ soil measurements, local-scale (∼50 m) longwave radar retrievals from NASA airborne P-band polarimetric SAR (PolSAR) and a remote-sensing-driven permafrost model. To better capture landscape variability in snow cover and its influence on the soil thermal regime, we downscaled global coarse-resolution (∼0.5∘) MERRA-2 reanalysis snow depth data using finer-scale (500 m) MODIS snow cover extent (SCE) observations. The downscaled 1 km snow depth data were used as key inputs to the permafrost model, capturing finer-scale variability associated with local topography and with favorable accuracy relative to the SNOTEL site measurements in Arctic Alaska (mean RMSE=0.16 m, bias=-0.01 role= presentation \u3ebias=−0.01 m). In situ tundra soil dielectric constant (ε) profile measurements were used for model parameterization of the soil organic layer and unfrozen-water content curve. The resulting model-simulated mean zero-curtain period was generally consistent with in situ observations spanning a 2∘ latitudinal transect along the Alaska North Slope (R: 0.6±0.2; RMSE: 19±6 days), with an estimated mean zero-curtain period ranging from 61±11 to 73±15 days at 0.25 to 0.45 m depths. Along the same transect, both the observed and model-simulated zero-curtain periods were positively correlated (R\u3e0.55, p\u3c0.01) with a MODIS-derived snow cover fraction (SCF) from September to October. We also examined the airborne P-band radar-retrieved ε profile along this transect in 2014 and 2015, which is sensitive to near-surface soil liquid water content and freeze–thaw status. The ε difference in radar retrievals for the surface (∼\u3c0.1 role= presentation \u3e∼\u3c0.1 m) soil between late August and early October was negatively correlated with SCF in September (R=-0.77 role= presentation \u3eR=−0.77, p\u3c0.01); areas with lower SCF generally showed larger ε reductions, indicating earlier surface soil freezing. On regional scales, the simulated zero curtain in the upper (\u3c0.4 m) soils showed large variability and was closely associated with variations in early cold-season snow cover. Areas with earlier snow onset generally showed a longer zero-curtain period; however, the soil freeze onset and zero-curtain period in deeper (\u3e0.5 m) soils were more closely linked to maximum thaw depth. Our findings indicate that a deepening active layer associated with climate warming will lead to persistent unfrozen conditions in deeper soils, promoting greater cold-season soil carbon loss

Evaluation of MERRA Land Surface Estimates in Preparation for the Soil Moisture Active Passive Mission

The authors evaluated several land surface variables from the Modern-Era Retrospective Analysis for Research and Applications (MERRA) product that are important for global ecological and hydrological studies, including daily maximum (Tmax) and minimum (Tmin) surface air temperatures, atmosphere vapor pressure deficit (VPD), incident solar radiation (SWrad), and surface soil moisture. The MERRA results were evaluated against in situ measurements, similar global products derived from satellite microwave [the Advanced Microwave Scanning Radiometer for Earth Observing System (EOS) (AMSR-E)] remote sensing and earlier generation atmospheric analysis [Goddard Earth Observing System version 4 (GEOS-4)] products. Relative to GEOS-4, MERRA is generally warmer (~0.5°C for Tmin and Tmax) and drier (~50 Pa for VPD) for low- and middle-latitude regions (\u3c50°N) associated with reduced cloudiness and increased SWrad. MERRA and AMSR-E temperatures show relatively large differences (\u3e3°C) in mountainous areas, tropical forest, and desert regions. Surface soil moisture estimates from MERRA (0–2-cm depth) and two AMSR-E products (~0–1-cm depth) are moderately correlated (R ~ 0.4) for middle-latitude regions with low to moderate vegetation biomass. The MERRA derived surface soil moisture also corresponds favorably with in situ observations (R = 0.53 ± 0.01, p \u3c 0.001) in the midlatitudes, where its accuracy is directly proportional to the quality of MERRA precipitation. In the high latitudes, MERRA shows inconsistent soil moisture seasonal dynamics relative to in situ observations. The study’s results suggest that satellite microwave remote sensing may contribute to improved reanalysis accuracy where surface meteorological observations are sparse and in cold land regions subject to seasonal freeze–thaw transitions. The upcoming NASA Soil Moisture Active Passive (SMAP) mission is expected to improve MERRA-type reanalysis accuracy by providing accurate global mapping of freeze–thaw state and surface soil moisture with 2–3-day temporal fidelity and enhanced (≤9 km) spatial resolution

Unsupervised Deraining: Where Contrastive Learning Meets Self-similarity

Image deraining is a typical low-level image restoration task, which aims at decomposing the rainy image into two distinguishable layers: the clean image layer and the rain layer. Most of the existing learning-based deraining methods are supervisedly trained on synthetic rainy-clean pairs. The domain gap between the synthetic and real rains makes them less generalized to different real rainy scenes. Moreover, the existing methods mainly utilize the property of the two layers independently, while few of them have considered the mutually exclusive relationship between the two layers. In this work, we propose a novel non-local contrastive learning (NLCL) method for unsupervised image deraining. Consequently, we not only utilize the intrinsic self-similarity property within samples but also the mutually exclusive property between the two layers, so as to better differ the rain layer from the clean image. Specifically, the non-local self-similarity image layer patches as the positives are pulled together and similar rain layer patches as the negatives are pushed away. Thus the similar positive/negative samples that are close in the original space benefit us to enrich more discriminative representation. Apart from the self-similarity sampling strategy, we analyze how to choose an appropriate feature encoder in NLCL. Extensive experiments on different real rainy datasets demonstrate that the proposed method obtains state-of-the-art performance in real deraining.Comment: 10 pages, 10 figures, accept to 2022CVP