Land-atmosphere Energy Exchange Research Articles

An Overview of the Role of Forests in Climate Change Mitigation

Nowadays, climate change is recognized as one of the biggest problems the world is facing, posing a potential threat to the environment and almost all aspects of human life. Since the United Nations Framework Convention on Climate Change in 1992, many efforts have been made to mitigate climate change, with no considerable results. According to climate change projections, temperatures will continue to rise, and extreme weather events will become more frequent, prolonged, and intense. Reflecting these concerns, the 2015 Paris Agreement was adopted as the cornerstone for reducing the impact of climate change, aiming to limit global warming below 2 °C and even keep the temperature rise below 1.5 °C. To achieve this international goal, focused mitigation actions will be required. Climate change has a strong impact on forests, enhancing their growth but also posing risks to them. Conversely, forests can mitigate climate change, as they have a considerable impact on global surface temperatures through their influence on the land–atmosphere energy exchange and the absorption of vast amounts of CO2 through photosynthesis. Consequently, afforestation and reforestation have become integral components of climate change mitigation strategies worldwide. This review aims to summarize the cutting-edge knowledge on the role of forests in climate change mitigation, emphasizing their carbon absorption and storage capacity. Overall, the impact of afforestation/reforestation on climate change mitigation hinges on strategic planning, implementation, and local forest conditions. Integrating afforestation and reforestation with other carbon removal technologies could enhance long-term effectiveness in carbon storage. Ultimately, effective climate change mitigation entails both restoring and establishing forests, alongside reducing greenhouse gas emissions.

A Potential Seasonal Predictor for Summer Rainfall over Eastern China: Spring Eurasian Snowmelt

Abstract The hydrological effect of snow over Eurasia is important for regulating regional and global climate through affecting land–atmosphere energy exchange. Based on observational and reanalysis datasets, this study investigates the effect of spring Eurasian snowmelt on the following summer rainfall over eastern China during the period of 1979–2018. The results show that a substantial meridional dipole pattern of summer rainfall anomalies over eastern China is closely associated with the preceding spring snowmelt anomalies over Eurasia, especially over remote Siberia. Excessive snowmelt anomalies over Siberia in spring could result in a wetter local soil condition from spring until the following summer, thereby increasing latent heat fluxes and reducing local surface temperature, and vice versa. Then, the anomalous summer surface cooling over Siberia increases the meridional gradient of temperature between the Eurasian midlatitudes and high latitudes, which intensifies the Eurasian atmospheric baroclinicity and motivates the eddy‐induced geopotential height responses along with the significant wave propagations spreading from the Eurasian high latitudes to Lake Baikal. As a result, excessive spring snowmelt anomalies over Siberia tend to be accompanied with an anomalous anticyclone circulation to the east of Lake Baikal and an anomalous cyclonic circulation over southeastern China in the following summer. This could lead to a meridional dipole pattern of summer rainfall anomalies over eastern China, with deficient rainfall over northern China and slightly excessive rainfall over southern China. The present findings highlight the lagged effect of spring Eurasian snowmelt on summer climate over eastern China, with implications for the regional seasonal climate prediction. Significance Statement Frequent summer droughts and floods over eastern China cause serious damage to the regional economy and society. This study reveals an important effect of spring Eurasian snowmelt anomalies on the following summer rainfall anomalies over eastern China. Through affecting the local land–atmosphere energy exchange and regulating the remote atmospheric circulation, excessive spring Eurasian snowmelt anomalies could induce a substantial meridional dipole pattern of summer rainfall anomalies over eastern China, with deficient rainfall over northern China and excessive rainfall over southern China, and vice versa. Therefore, this work identifies spring Eurasian snowmelt as a potential seasonal predictor for summer rainfall over eastern China, which is home to more than a billion people. This is important for the local livelihood, including agriculture, water resources, ecosystems, human health, and economies.

Modeling land-atmosphere energy and water exchanges in the typical alpine grassland in Tibetan Plateau using Noah-MP

Study regionAlpine grassland in the Tibetan Plateau Study focusThis study used Noah-MP to simulate the energy fluxes [net radiation (Rn), latent (λE) and sensible (H) heat fluxes] in two typical alpine ecosystems in the Tibetan Plateau. The effectivenesses of new parameterization schemes for the roughness length for heat transfer and the root distribution were evaluated. By further using multi-scenario modeling, the responses of land evapotranspiration (ET), soil evaporation (Es) and vegetation transpiration (Tr) to changes in precipitation, solar radiation and air temperature were investigated. New hydrological insights for the regionThe default Noah-MP largely overestimates (underestimates) Rn and λE, but tends to underestimate (overestimate) H in alpine meadow (alpine steppe). After incorporating two newly-developed parameterization schemes, the accurancy of Noah-MP has been improved in alpine meadow for Rn and λE, while in alpine steppe only H has been improved. The ET, Es and Tr in alpine meadow are more sensitive to decreasing precipitation than that to increasing precipitation. In alpine steppe, ET and Es increase with increasing precipitation, while Tr responds weakly to changes in precipitation; The effect of temperature change on ET appears weak in both ecosystems. However, Tr shows negative responses to increased temperature and such responses in alpine steppe are more remarkable than those in alpine meadow, suggesting the former may be more vulnerable to future warming than the latter.

Estimation of Daily Mean Land Surface Temperature over the Qinghai–Tibet Plateau Based on an RTM-DTC Model

Accurately estimating daily mean land surface temperature (LST) is crucial for studying the urban heat island effect, land–atmosphere energy exchange, and global climate change. However, limited research has been conducted on average surface temperature estimation, particularly in high-altitude regions like the Qinghai–Tibet Plateau with extensive cloud cover. In this study, we propose the Reanalysis Data and Thermal Infrared Remote Sensing Data Merging-Diurnal Temperature Cycle (RTM-DTC) model specifically for the Qinghai–Tibet Plateau, successfully estimating mean LST using the model. We apply the RTM method to reconstruct LST under cloud cover from the MODIS LST product and calculate the average temperature using the DTC model. Validation with in situ measurements from seven meteorological stations on the Tibetan Plateau yielded daily scale RMSEs ranging from 1.81 K to 2.021 K and monthly scale RMSEs ranging from 1.77 K to 2.0 K, with an average RMSE of 1.91 K. These results demonstrate the adaptability of the RTM-DTC model and its ability to depict the annual variation curve of the mean surface temperature, and provide further research on RTM-DTC as a valuable approach.

Lagged feedback of peak season photosynthetic activities on local surface temperature in Inner Mongolia, China

Increased vegetation peak growth and phenological shifts toward spring have been observed in response to climate warming in the temperate regions. Such changes have the potential to modify warming by perturbing land‒atmosphere energy exchanges; however, the signs and magnitudes of biophysical feedback on surface temperature in different biomes are largely unknown. Here, we synthesized information from vegetation growth proxies, land surface temperature (LST), and surface energy balance factors (surface evapotranspiration (ET), albedo, and broadband emissivity (BBE)) to investigate the variations in timing (PPT) and productivity (PPmax) of seasonal peak photosynthesis and their time-lagged biophysical feedbacks to the post-season LST in Inner Mongolia (IM) during 2001–2020. We found that increased PPmax, rather than advanced PPT, exhibited a significant impact on LST, with divergent signs and magnitudes across diurnal periods and among different biomes. In the grassland biome, increased PPmax cooled both LST during daytime (LSTday) and nighttime (LSTnight) throughout the post-season period, with a more pronounced response during daytime and diminishing gradually from July to September. This cooling effect on LST was primarily attributed to enhanced ET, as evidenced by the greater effect of ET cooling than that of albedo warming and BBE cooling based on a structural equation model (SEM). In the forest biome, increased PPmax led to a symmetrical warming effect on LSTday and LSTnight, and none of the surface energy balance factors were identified as significant intermediate explanatory factors for the observed warming effect. Moreover, the responses of average LST (LSTmean) and diurnal temperature range of LST (LSTDTR) to variations in PPmax were consistent with those of LSTday at two biomes. The observations above elucidate the divergent feedback mechanisms of vegetation peak growth on LST among different biomes and diurnal cycles, which could facilitate the improvement of the realistic parameterization of surface processes in global climate models.

Interannual variability of spring and summer monsoon growing season carbon exchange at a semiarid savanna over nearly two decades

Eddy covariance measurements of land-atmosphere energy, carbon, and water exchange now span multiple decades at some sites, supporting an improved understanding of flux interannual variability (IAV) and its ecophysiological and physical controls. Most eddy covariance IAV studies have focused on temperate forest ecosystems, where carbon fluxes are large and flux records are longest – but also where IAV is much lower than in dryland regions, which have been identified as an essential driver of the trend and variability in the global terrestrial carbon sink. In this study, we leveraged 19 years of continuous micrometeorological measurements at the AmeriFlux US-SRM mesquite savanna site in southern Arizona, USA to quantify the IAV, trends, and drivers of carbon fluxes during the distinct spring and summer growing seasons. We also assessed the ability of modern satellite and land surface models to capture the IAV of seasonal water and carbon fluxes. Annual net ecosystem production (NEP) was small and highly variable (23 +/- 64 gC m − 2 yr−1). Precipitation and associated measures of water availability determined most of the variability in NEP, largely through their influence on annual and seasonal gross ecosystem productivity (GEP) as opposed to ecosystem respiration (ER). Root-zone soil moisture captured between 73% (spring) and 85% (summer) of GEP variability and between 73% (spring) and 58% (summer) of ER variability. Throughout the study period, soil moisture and greenness increased with associated increases in GEP, ER and NEP. These trends were strongly influenced by very productive and wet summer growing seasons during the last two years, which were characterized by abundant understory grass cover. Typically, less than half of the variability in growing season GEP and evapotranspiration was captured by satellite-based estimates and land surface model simulations with local site forcing and calibration, highlighting the ongoing utility of long-term datasets to support careful model testing and improvement.

Two-level eddy covariance measurements reduce bias in land-atmosphere exchange estimates over a heterogeneous boreal forest landscape

Estimates of land-atmosphere exchanges of carbon, energy, water vapor, and other greenhouse gases based on the eddy covariance (EC) technique rely on the fundamental assumption that the flux footprint area is homogeneous. We investigated the impact of source area heterogeneity on flux estimates in single-level EC measurements over a managed boreal forest landscape. For this purpose, we compared single-level measurements with those from a two-level approach consisting of concurrent EC measurements at 60 and 85 m above the ground. This two-level set-up provided a unique opportunity to obtain nearly congruent diel footprint areas by combining data from the higher and lower levels during day- and nighttime, respectively.We found that the variation in the averaged footprint area between day- and nighttime was reduced by up to 89% in the two-level approach compared to the single-level data at the higher level (85 m). Considering spring, summer, and fall months, the resulting relative potential bias in flux observations due to landscape heterogeneity was highest at short time steps (≤ daily) ranging between 35% and 325% for half-hourly data. During winter months, when stable atmospheric regimes prevailed during day and night, the footprints within the diel course nearly overlapped also at a given single level and hence no improvement of flux estimates was found. The absolute cumulated sums for the study period (excluding winter months) of gross primary production, ecosystem respiration, latent heat, and sensible heat flux were underestimated by about 28%, 52%, 5%, and 3%, respectively, whereas that of net ecosystem CO2 exchange was overestimated by about 109% in the single-level approach. Overall this study suggests that footprint heterogeneity may introduce considerable bias in single-level flux estimates — particularly at short time scales — with large implications for model-data fusion studies, site comparisons, and up- or downscaling of land-atmosphere exchange processes.

Reconstruction of Global Long-Term Gap-Free Daily Surface Soil Moisture from 2002 to 2020 Based on a Pixel-Wise Machine Learning Method

Global, long-term, gap-free, high quality soil moisture products are extremely important for hydrological monitoring and climate change research. However, soil moisture products produced from satellite observations have data gaps due to the limited capabilities of satellite orbit/swath and retrieval algorithms, which limit the regional and global applications of soil moisture data in hydrology and agriculture studies. To solve this problem, we proposed a gap-filling method to reconstruct a global gap-free surface soil moisture product by applying the machine learning (Random Forest) algorithm on a pixel-by-pixel basis, taking into account the nonlinear relationship between surface soil moisture and the related surface environmental variables. The gap-filling method was applied to the NN-SM surface soil moisture product, which has a fraction of data gaps of around 50% globally on a multi-year average. A global daily gap-free surface soil moisture dataset from 2002 to 2020 was then generated. The reconstructed values of several sub-regions after manually eliminating the original values were cross-verified with the original data, and this clearly demonstrated the reliability of the reconstruction method with the correlation coefficient (R) ranging between 0.770 and 0.918, the Root Mean Square Error (RMSE) between 0.057 and 0.082 m3/m3, the unbiased Root Mean Square Error (ubRMSE) between 0.053 and 0.081 m3/m3, and Bias between −0.012 and 0.008 m3/m3. The accuracy of the reconstructed surface soil moisture dataset was evaluated using in situ observations of surface soil moisture at 12 sites from the International Soil Moisture Network (ISMN) and the Long-Term Agroecosystem Research (LTAR) network, and the results showed good accuracy in terms of R (0.610), RMSE (0.067 m3/m3), ubRMSE (0.045 m3/m3) and Bias (0.031 m3/m3). Overall, the reconstructed surface soil moisture dataset retained the characteristics of the NN-SM product, such as high accuracy and good spatiotemporal pattern. However, with the advantage of continuous spatiotemporal coverage, it is more suitable for further applications in the analysis of global surface soil moisture trends, land surface hydrological processes, and land-atmosphere energy and water exchanges, etc.

Diagnosing the compound seasonal soil moisture-hydroclimate interaction regime on the Tibetan Plateau using multi-high-resolution reanalysis products and one regional climate model

Land-atmosphere energy and moisture exchanges exert great impacts on local and regional climate. However, high uncertainties exist in the recent land–atmosphere interactions due to scarce observations and difficulties in modeling at high elevations and cold regions. This study applied multiple high-resolution reanalysis products (ERA5-land, GLDAS Noah and CLSM products) and one regional climate model, RegCM4, to diagnose the interaction pattern and strength of seasonal soil moisture and hydroclimate on the Tibetan Plateau (TP) using a conditional correlation coefficient method and the global land–atmosphere coupling experimental modeling approach. A compound aggregation method is applied to recognize hot spots with strong soil moisture-hydroclimate interactions among multiple data. As a result, the Changtang Plateau in the inner TP, the sources of the Yellow, Yangtze, Mekong, Salween, and Lancang Rivers and part of the Brahmaputra River basin in the south-southeastern TP are diagnosed with strong land–atmosphere interactions in most seasons. In summer, the coupling strength is stronger, and the covered area is much wider and spatially continuous as the climate turns warm. All applied data indicate a positive dominant soil moisture-precipitation feedback, and a negative soil moisture-temperature feedback at the hot spots. Meanwhile, the soil moisture-runoff feedback is strong and positive at most parts of the TP, especially at the hot spots and in summer. Evapotranspiration acts as a positive linkage in the soil moisture-precipitation-temperature interactions, while the nexus of soil moisture, evapotranspiration and runoff is complicated. This study contributes to understanding local land-hydroclimate interactions in the high elevation and cold climate of the TP. Additionally, the found the proposed compound aggregation method efficient for compound studies.

Estimating Hourly All-Weather Land Surface Temperature From FY-4A/AGRI Imagery Using the Surface Energy Balance Theory

Thermal infrared (TIR) observations from geostationary satellites enable the retrievals of the diurnal variations in land surface temperatures (LST) linked to the land-atmosphere energy exchange and water cycle. However, cloud cover obstructs TIR signals and leads to missing gaps that generally account for more than 50% of TIR LST maps. This study proposed an effective method to estimate hourly all-weather LST from the Advanced Geosynchronous Radiation Imager (AGRI) data under the framework of surface energy balance (SEB) theory. An improved temperature and emissivity separation algorithm was first used to obtain the high-quality clear-sky LST, which plays a decisive role in ensuring the accuracy of recovered cloudy-sky LST. Then we proposed a unique way to solve the temperature difference (ΔLST) between the cloudy-sky LST and hypothetical clear-sky LST caused by cloud radiative effects (CRE). The bias (RMSE) of the estimated AGRI hourly cloudy-sky LST is 0.10 K (3.71 K) during the daytime, and -0.20 K (2.73 K) during the nighttime, respectively. The overall bias (RMSE) of the estimated AGRI all-weather LST is 0.02 K (2.84 K). The estimated hourly all-weather LST not only captures the rapid variation in diurnal LST but is also promising for temporal upscaling. The temporally upscaled daily mean LST shows a bias (RMSE) of 0.03 K (1.35 K). This study provides a promising solution to generate diurnal hourly all-weather LST for AGRI and other geostationary satellites.

Land-Atmosphere Energy Exchange Characteristics in Ali of Tibetan

Based on the comprehensive data from the land-atmosphere interaction observation station in Ali of Tibetan in 2019, the characteristics of land-atmosphere energy exchange processes in Ali were analyzed. The results indicated that the timing of the mean intraday net radiation peak in Ali over the past 20 years has been delayed, and the month when the maximum monthly mean net radiation occurred has been delayed by about 2 months; the maximum daily mean, maximum monthly mean, minimum monthly mean, and annual mean sensible heat were 99.63 w/m2, 76.53 w/m2, 17.47 w/m2, and 46.74 w/m2, respectively, and the maximum daily mean, maximum monthly mean, minimum monthly mean, and annual mean latent heat flux were 73.27 w/m2, 36.13 w/m2, 0.67 w/m2, and 8.32 w/m2, respectively; and the monthly mean sensible heat was greater than the latent heat in all months.

Biophysical impacts of northern vegetation changes on seasonal warming patterns

The seasonal greening of Northern Hemisphere (NH) ecosystems, due to extended growing periods and enhanced photosynthetic activity, could modify near-surface warming by perturbing land-atmosphere energy exchanges, yet this biophysical control on warming seasonality is underexplored. By performing experiments with a coupled land-atmosphere model, here we show that summer greening effectively dampens NH warming by −0.15 ± 0.03 °C for 1982–2014 due to enhanced evapotranspiration. However, greening generates weak temperature changes in spring (+0.02 ± 0.06 °C) and autumn (−0.05 ± 0.05 °C), because the evaporative cooling is counterbalanced by radiative warming from albedo and water vapor feedbacks. The dwindling evaporative cooling towards cool seasons is also supported by state-of-the-art Earth system models. Moreover, greening-triggered energy imbalance is propagated forward by atmospheric circulation to subsequent seasons and causes sizable time-lagged climate effects. Overall, greening makes winter warmer and summer cooler, attenuating the seasonal amplitude of NH temperature. These findings demonstrate complex tradeoffs and linkages of vegetation-climate feedbacks among seasons.

Effect of dust aerosols on the heat exchange over the Taklimakan Desert

The Taklimakan Desert (TD), an important dust source over the world, has a significant impact on the weather and climate. In this study, the effect of dust aerosols on the heat exchange of Earth-Atmosphere system during a dust event over the TD is investigated by WRF-Chem model. The result shows that, during the dust event from 25–28 July 2019, the dust injected from the TD can be transported up to an altitude more than 6 km in the atmosphere. Over the TD, most portion of the surface net radiation (RN) is distributed to the sensible heat flux (SF), especially over the areas with dust emission and high dust concentration. The existence of dust aerosols can affect the RN and further influence the other components of the surface-air energy exchange. The RN is enhanced as well as the SF than that without dusts where emits dust strongly. While the dusts make the RN and SF lower than that without dusts during the dust transportation. The effect of dust on RN and SF varies at different stages of the dust event, and it causes consistent changes in RN and SF. Besides, the latent heat flux (LF) exchange has a similar distribution with the SF over the TD except that the dusts exert less impact on LF. Additionally, the soil heat flux (GF) shows an opposite pattern to the SF, and the dust aerosols have less influence on the GF. Vertically, the shortwave radiation heating caused by dust aerosols over TD during the dust event is positive with a maximum of 0.25 K day−1 below an altitude of 6 km. Though some uncertainties may be introduced in the modeling, this study can provide a basis to understand the effects of dusts on the land-atmosphere energy exchange over the TD.

Evapotranspiration and energy partitioning of a typical alpine wetland in the central Tibetan Plateau

The alpine wetlands in the Tibetan Plateau (TP) account for more than 30% of the wetlands in China. As an ecosystem that is particularly sensitive to the changing climate, the rapid warming of the TP may have a significant impact on the growth of vegetation in these cold wetlands. However, due to the lack of ground observations, it remains unclear how the land-atmosphere exchanges of water and energy develop in such special alpine ecosystems. This limits the understanding of how eco-hydrological processes respond to environmental changes. In this study, we used ground measured data to investigate the dynamics of energy partitioning and water budget over a typical wetland in the central TP over two hydrological years. The results demonstrate that the seasonal variations in land-atmosphere energy exchange were significant during the observation period. In the growing season, the available energy was mainly consumed by latent heat flux (LE), with the mean ratio of LE to net radiation (Rn) exhibiting values of 0.68 and 0.76 in 2019 and 2020, respectively. During the freezing period, the available energy was primarily consumed by sensible heat flux (H), with mean ratio of H to Rn values of 0.69 and 0.68 in 2019 and 2020, respectively. The total annual evapotranspiration (ET) (include sublimation) in 2019 and 2020 were 652.5 mm and 706.6 mm, respectively. Annual ET was found to be higher than precipitation (P), indicating that soil water was supplied via lateral flow from the melting of upstream glaciers and snow, supporting ET from the wetland. During the growing season, ET was mainly controlled by solar radiation but became more strongly coupled with soil water during the freezing period. Evaluations of four typical gridded ET products suggest that the Penman–Monteith–Leuning (PML) model showed the best performance with the highest Nash-Sutcliffe Efficiency (NSE) when compared with the ground-measured ET rates, indicating that this product may be appropriate for use in similar data-scarce regions. The land-atmosphere energy and water exchanges in the alpine wetland are important for an improved understanding of the hydrological cycle of the Asian Water Tower and could be used as a reference for further ET model parameterizations and validations.

Estimation of daily mean land surface temperature at global scale using pairs of daytime and nighttime MODIS instantaneous observations

Accurate estimations of daily mean land surface temperature (LST) are important for investigating the urban heat island effect, land-atmosphere energy exchanges, and global climate change. Moderate Resolution Imaging Spectroradiometer (MODIS) sensors can provide up to four instantaneous LSTs of a single day across the world. However, numerous studies, such as those on climate change and hydrology, require the input of daily mean LSTs rather than instantaneous value. In this paper, we propose a practical method to estimate the daily mean LST using instantaneous LST products derived from MODIS. Based on the in situ LST measurements collected from 235 sites distributed globally, multiple linear regressions of two to four valid instantaneous LSTs at different MODIS observations moments (at least one daytime and one nighttime observations) can provide reliable estimates of daily mean LSTs under all-weather conditions with a root mean square error (RMSE) of less than 1.60 K. In addition, the conditions of clouds would affect the estimation accuracy of daily mean LST to a certain extent. Subsequently, an algorithm is proposed to produce the most complete coverage of daily mean LSTs from instantaneous LST products derived from MODIS. Validation results with in situ measurements show that the daily mean LSTs estimated from the MOD11A1 and MYD11A1 products are similar to the daily mean of the in situ LST, with an RMSE of 2.17 K. Furthermore, the daily mean LST derived from MODIS data is successfully applied to calculate the global annual cycle parameters (ACPs) in the annual temperature cycle (ATC) model. The results of this study show that the daily mean LST can be retrieved accurately from combinations of daytime and nighttime LSTs derived from MODIS. We expect that our findings will be useful for various applications involving global LST trend analysis and climate change.

Contributions of vegetation restoration and climate change to spatiotemporal variation in the energy budget in the loess plateau of china

Quantifying the impacts of vegetation restoration (VR) on energy exchange is essential for exploring climate-vegetation interactions. Due to the complexity of land–atmosphere energy exchange and limited observation networks, the influence of VR on the energy balance remains unclear. To improve the understanding of the energy budget change associated with VR in the Loess Plateau (LP), we performed a series of sensitivity simulation experiments using the Community Land Model within the Community Earth System Model, and the spatiotemporal responses of energy exchange to VR and the roles of VR in the variations in energy budget under climate change were investigated. The results showed that the land cover changes from 2000 to 2015 exerted a positive effect on net radiation (NR) and sensible heat (SH), and evident increases in annual mean NR and SH were found in the southeastern LP where larger-scale cropland was converted to grassland or forestland, with increasing magnitudes exceeding 2 W/m2. Latent heat (LH) increased only during May–July, particularly on the southern edge of LP. The increasing magnitudes of NR and LH under Representative Concentration Pathway (RCP)8.5 were larger than those under RCP4.5, with the greatest differences being approximately 4 W/m2 and 5 W/m2 during 2081–2100 for NR and LH, respectively. SH remained nearly stable under RCP4.5 and showed a weak decrease under RCP8.5. Under the same RCP, only slight differences in the energy budget components were detected between the land cover condition in 2015 and 2000, with the largest difference of 0.8 W/m2 in SH during 2021–2040. These results suggested that although both VR and climate warming exerted positive effects on NR, the impact of climate change was larger than that of VR in the LP, and a shift in the local surface energy budget toward LH would occur with climate warming.

Feedbacks between vegetation restoration and local precipitation over the Loess Plateau in China

The implementation of large-scale vegetation restoration over the Chinese Loess Plateau has achieved clear improvements in vegetation fraction, as evidenced by large areas of slopes and plains being restored to grassland or forest. However, such large-scale vegetation restoration has altered land-atmosphere exchanges of water and energy, as the land surface characteristics have changed. These variations could affect regional climate, especially local precipitation. Quantitatively evaluating this feedback is an important scientific question in hydrometeorology. This study constructs a coupled land-atmosphere model incorporating vegetation dynamics, and analyzes the spatio-temporal changes of different land use types and land surface parameters over the Loess Plateau. By considering the impacts of vegetation restoration on the water-energy cycle and on land-atmosphere interactions, we quantified the feedback effect of vegetation restoration on local precipitation across the Loess Plateau, and discussed the important underlying processes. To achieve a quantitative evaluation, we designed two simulation experiments, comprising a real scenario with vegetation restoration and a hypothetical scenario without vegetation restoration. These enabled a comparison and analysis of the net impact of vegetation restoration on local precipitation. The results show that vegetation restoration had a positive effect on local precipitation over the Loess Plateau. Observations show that precipitation on the Loess Plateau increased significantly, at a rate of 7.84 mm yr−2, from 2000 to 2015. The simulations show that the contribution of large-scale vegetation restoration to the precipitation increase was about 37.4%, while external atmospheric circulation changes beyond the Loess Plateau contributed the other 62.6%. The average annual precipitation under the vegetation restoration scenario over the Loess Plateau was 12.4% higher than that under the scenario without vegetation restoration. The above research results have important theoretical and practical significance for the ecological protection and optimal development of the Loess Plateau, as well as the sustainable management of vegetation restoration.

Multiyear in-situ L-band microwave radiometry of land surface processes on the Tibetan Plateau

We report a unique multiyear L-band microwave radiometry dataset collected at the Maqu site on the eastern Tibetan Plateau and demonstrate its utilities in advancing our understandings of microwave observations of land surface processes. The presented dataset contains measurements of L-band brightness temperature by an ELBARA-III microwave radiometer in horizontal and vertical polarization, profile soil moisture and soil temperature, turbulent heat fluxes, and meteorological data from the beginning of 2016 till August 2019, while the experiment is still continuing. Auxiliary vegetation and soil texture information collected in dedicated campaigns are also reported. This dataset can be used to validate the Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) satellite based observations and retrievals, verify radiative transfer model assumptions and validate land surface model and reanalysis outputs, retrieve soil properties, as well as to quantify land-atmosphere exchanges of energy, water and carbon and help to reduce discrepancies and uncertainties in current Earth System Models (ESM) parameterizations. Measurement cases in winter, pre-monsoon, monsoon and post-monsoon periods are presented.

Reforestation and surface cooling in temperate zones: Mechanisms and implications.

Land-use/cover change (LUCC) is an important driver of environmental change, occurring at the same time as, and often interacting with, global climate change. Reforestation and deforestation have been critical aspects of LUCC over the past two centuries and are widely studied for their potential to perturb the global carbon cycle. More recently, there has been keen interest in understanding the extent to which reforestation affects terrestrial energy cycling and thus surface temperature directly by altering surface physical properties (e.g., albedo and emissivity) and land-atmosphere energy exchange. The impacts of reforestation on land surface temperature and their mechanisms are relatively well understood in tropical and boreal climates, but the effects of reforestation on warming and/or cooling in temperate zones are less certain. This study is designed to elucidate the biophysical mechanisms that link land cover and surface temperature in temperate ecosystems. To achieve this goal, we used data from six paired eddy-covariance towers over co-located forests and grasslands in the temperate eastern United States, where radiation components, latent and sensible heat fluxes, and meteorological conditions were measured. The results show that, at the annual time scale, the surface of the forests is 1-2°C cooler than grasslands, indicating a substantial cooling effect of reforestation. The enhanced latent and sensible heat fluxes of forests have an average cooling effect of -2.5°C, which offsets the net warming effect (+1.5°C) of albedo warming (+2.3°C) and emissivity cooling effect (-0.8°C) associated with surface properties. Additional daytime cooling over forests is driven by local feedbacks to incoming radiation. We further show that the forest cooling effect is most pronounced when land surface temperature is higher, often exceeding -5°C. Our results contribute important observational evidence that reforestation in the temperate zone offers opportunities for local climate mitigation and adaptation.

Modeling Year-to-Year Variations of Clear-Sky Land Surface Temperature Using Aqua/MODIS Data

Land surface temperature (LST) and its annual or inter-annual variations play an important role in understanding global climate change, urban heat island, and the process of land-atmosphere energy exchange. Many annual temperature cycle (ATC) models [i.e., ATC with three or five parameters (ACP3 or ACP5)] have been proposed to analyze the annual variations of LST in the past decades. In this study, two year-to-year continuous and derivable models (YYCD_ACP3 and YYCD_ACP5 models) were proposed to model several years of ATCs. The fitting results of the YYCD_ACP3 model with global Aqua/MODIS daytime LSTs from 2014 to 2018 show that the YYCD_ACP3 model achieved a good performance in fitting the time-series LSTs with an overall normalized root-mean-square error (NRMSE) of 0.21, coefficient of determination (R 2 ) of 0.74, and refined index of agreement ($d$ ) of 0.85. In addition, the modeling results of ten representative samples covering different climatic conditions and land cover worldwide show that, except for two sites located in tropical and Antarctic, the YYCD_ACP3 model could show a good performance with R 2 greater than 0.6. Although the ACP3 model shows similar performance to the YYCD_ACP3 model, the fitting curve of the YYCD_ACP3 model is continuous and smooth for describing the interannual variations of LST. When the LSTs of 2014-2018 are fitted as a whole by using both models, the YYCD_ACP3 model shows a slightly better performance than that of the ACP3 model. The application of the YYCD_ACP3 model with the global MODIS LSTs from 2003 to 2018 indicates that the results of the YYCD_ACP3 model have the potential to reveal the interannual variations of LST. Therefore, we conclude that the YYCD models are valuable for modeling the variations of LST over several years and can be widely applied.