Land Atmosphere Coupling Research Articles

Environmental factors driving evapotranspiration over a grassland in a transitional climate zone in China

AbstractThe surface evapotranspiration (ET) process is the key link in the interaction between land and atmosphere. However, the influence of different environmental factors on ET over transitional climate zones and the physical pattern of the interaction between multiple factors remain unclear. Therefore, based on the continuous observation data during the vegetation growing season of a typical grassland in the Semi‐Arid Climate and Environment Observation of Lanzhou University (SACOL) station from 2007 to 2012, the influence pattern of multiple environmental factors on ET over China's transitional climate zone was analysed. Each environmental factor exhibited significant seasonal and interannual variations. The mean value of ET was 1.67 mm day−1. Although the maximum values of sensible heat flux and vapour pressure deficit occurred in April and June, respectively, the maximum values of other environmental factors appeared from July to August. Net radiation, soil moisture, and normalized difference vegetation index (NDVI) were the main controlling factors of ET over the grassland, with correlation coefficients of 0.54, 0.52, and 0.46, respectively. The analysis of multiple environmental factors showed that when soil moisture, wind speed, net radiation, and NDVI reached 0.2 m3 m−3, 2 m s−1, 100 W m−2, and 0.2, respectively, ET varied in contrast with vapour pressure, vapour pressure deficit, and air temperature under the influences of weather processes, land–atmosphere coupling, and drought stress. These findings deepen our understanding of the role of ET in the land–atmosphere coupling process over the transitional climate zone in China.

Understanding and reducing the uncertainties of land surface energy flux partitioning within CMIP6 land models

Land surfaces dissipate energy through latent (LE) and sensible (H) heat fluxes that modulate atmospheric temperature and humidity, which in return affect land surface vegetation and soil processes. Within this two-way land-atmosphere coupling, surface energy partitioning (LE versus H) plays a central role in connecting the land and atmosphere states and fluxes. However, considerably large uncertainties still exist in earth system land models, i.e. the phase 6 of the Coupled Model Intercomparison Project (CMIP6). Further, the underlying controls from climate and biological factors on surface energy partitioning over different biome types are not well understood. In this study, we combined machine learning (ML) and causal inference models to investigate and reduce the uncertainties (i.e., parametric, structural, and forcing uncertainties) of CMIP6 simulated evaporative fraction (defined as LE / (LE + H)) across 64 FLUXNET sites that cover five major biomes. We found that CMIP6 model ensembles overestimated evaporative fraction with considerable spread at deciduous broadleaf forest, evergreen needleleaf forest, and savanna sites. Accounting for the biases from all related surface climate and biological driving variables, the CMIP6 model simulated EF could be largely improved (e.g., R between model and observed EF improved from 0.47 to 0.66), with leaf area index, vapor pressure deficit, and precipitation dominated the model improvement. Furthermore, ML-based parameterization generally showed a promising opportunity to further reduce model biases (e.g., R improved from 0.66 to 0.80) in spite of the limited improvement at evergreen broadleaf forest sites where model bias may be dominated by structural imperfection. This study provided an effective framework to understand and reduce model uncertainties in simulating land surface energy flux partitioning and, more importantly, highlighted the need of effective model structure improvement for the next generation earth system land model development.

Hydrothermal Factors Influence on Spatial-Temporal Variation of Evapotranspiration-Precipitation Coupling over Climate Transition Zone of North China

As a land–atmosphere coupling “hot spot”, the northern China climate transition zone has a sharp spatial gradient of hydrothermal conditions, which plays an essential role in shaping the spatial and temporal pattern of evapotranspiration-precipitation coupling, but whose mechanisms still remain unclear. This study analyzes the spatial and temporal variation in land–atmosphere coupling strength (CS) in the climate transitional zone of northern China and its relationship with soil moisture and air temperature. Results show that CS gradually transitions from strong positive in the northwest to negative in the southeast and northeast corners. The spatial distribution of CS is closely related to climatic hydrothermal conditions, where soil moisture plays a more dominant role: CS increases first, and then decreases with increasing soil moisture, with the threshold of soil moisture at 0.2; CS gradually transitions from positive to negative at soil moisture between 0.25 and 0.35; CS shows an exponential decreasing trend with increasing temperature. In terms of temporal variation, CS is strongest in spring and weakens sequentially in summer, autumn, and winter, and has significant interdecadal fluctuations. The trend in CS shifts gradually from significantly negative in the west to a non-significant positive in the east. Soil moisture variability dominates the intra-annual variability of CS in the study regions, and determines the interannual variation of CS in arid and semi-arid areas. Moreover, the main reason for the positive and negative spatial differences in CS in the study area is the different driving regime of evapotranspiration (ET). ET is energy-limited in the southern part of the study area, leading to a positive correlation between ET and lifting condensation level (LCL), while in most of the northern part, ET is water-limited and is negatively correlated with LCL; LCL has a negative correlation with P across the study area, thus leading to a negative ET-P coupling in the south and a positive coupling in the north.

Improving the prediction of monsoon depressions by assimilating ASCAT soil moisture in NCUM-R modeling system

The present study investigates the impact of assimilation of satellite derived soil wetness data from Advanced Scatterometer (ASCAT) on simulation of inland moving monsoon depressions (MDs) over Indian region using high resolution NCUM-R forecasting system. The simplified Extended Kalman Filter (sEKF) based Land Surface Data Assimilation (LSDA) system is used to create land surface soil moisture initial conditions for NCUM-R by assimilating ASCAT soil moisture (SM) along with screen level observations. Two numerical experiments, namely CTL (incorporating only screen level observations in LSDA) and ASCAT (assimilating both ASCAT SM and screen level observations in LSDA) are carried out to update the model soil moisture initial conditions. Identical atmospheric initial condition prepared considering the 4DVAR data assimilation technique is used in both experiments.The results of this study show that the assimilation of remotely sensed SM has a beneficial impact on MD simulation, its movement, structure and associated precipitation. The analysis increment of fluxes from ASCAT experiment clearly shows properly coupled positive feedback between land and atmosphere. SM in the vicinity of MD is considerably improved in the ASCAT analysis as compared to the CTL analysis and is well correlated with the observed rainfall over the regions. The root mean square error of top layer SM is decreased by 5 to 15% in ASCAT analyses compared to CTL analyses. The land-atmosphere coupling feedback is investigated by using Dirmeyer's indices, it is noticed that the precipitation modulated by the latent heat flux is higher in ASCAT simulation than in the CTL simulation. This improvement is due to the proper representation of surface conditions by assimilating the satellite SM observations into LSDA. The contribution of individual terms of vorticity equation and total vorticity tendency is considerably well represented in ASCAT experiment as compared to the CTL experiment during the various phases of MD. Overall, this study indicates that the use of ASCAT SM in the preparation of soil moisture initial conditions helps to improve the forecast of MDs by the regional NWP system.

Characterization of seasons over the extratropics based on the annual daily mean temperature cycle

AbstractA proposal to characterize seasons based on the annual cycle of daily mean temperature over extratropical regions is presented. Four metrics are computed, based on the dates along the year when the maximum and minimum temperature values and the maximum warming and cooling (when the rate of temperatura change is maximum or minimum) are obtained. These four metrics are computed for 1980–2014 period for ERA‐Interim, JRA‐55 and NCEP2 reanalysis products. Results indicate that these four metrics are able to represent, with geographical coherence over any extratropical region, the start and end of subperiods along the year and that can be seen and used as a consistent procedure to define seasons. The two more extreme metrics (dates for the maximum and minimum temperature) are closely influenced by the variability of the net radiation and near‐surface circulation. Regions with different climatic regimes, defined in terms of land–atmosphere coupling conditions, have different contributing factors influencing the interannual variability of the metrics. In humid regions the net surface radiation is critical for the timing of the maximum temperature date, while the variability of the atmospheric circulation is relevant for the minimum temperature metric. In transitional regions, land–atmosphere interaction plays a key role in the timing of the annual cycle, both in summer and winter. Variations in net radiation are relevant for dry regions. Therefore, a robust methodology to establish seasons based on the annual temperature cycle is presented. We also inspect how the metrics timing responds in terms of the interannual variations of different relevant atmospheric variables.

Spatially varying relevance of hydrometeorological hazards for vegetation productivity extremes

Abstract. Vegetation plays a vital role in the Earth system by sequestering carbon, producing food and oxygen, and providing evaporative cooling. Vegetation productivity extremes have multi-faceted implications, for example on crop yields or the atmospheric CO2 concentration. Here, we focus on productivity extremes as possible impacts of coinciding, potentially extreme hydrometeorological anomalies. Using monthly global satellite-based Sun-induced chlorophyll fluorescence data as a proxy for vegetation productivity from 2007–2015, we show that vegetation productivity extremes are related to hydrometeorological hazards as characterized through ERA5-Land reanalysis data in approximately 50 % of our global study area. For the latter, we are considering sufficiently vegetated and cloud-free regions, and we refer to hydrometeorological hazards as water- or energy-related extremes inducing productivity extremes. The relevance of the different hazard types varies in space; temperature-related hazards dominate at higher latitudes with cold spells contributing to productivity minima and heat waves supporting productivity maxima, while water-related hazards are relevant in the (sub-)tropics with droughts being associated with productivity minima and wet spells with the maxima. Alongside single hazards compound events such as joint droughts and heat waves or joint wet and cold spells also play a role, particularly in dry and hot regions. Further, we detect regions where energy control transitions to water control between maxima and minima of vegetation productivity. Therefore, these areas represent hotspots of land–atmosphere coupling where vegetation efficiently translates soil moisture dynamics into surface fluxes such that the land affects near-surface weather. Overall, our results contribute to pinpointing how potential future changes in temperature and precipitation could propagate to shifting vegetation productivity extremes and related ecosystem services.

Observational evidence of regional increasing hot extreme accelerated by surface energy partitioning

AbstractLand-atmosphere interactions play an important role in the changes of extreme climates, especially in hot spots of land-atmosphere coupling. One of the linkages in land-atmosphere interactions is the coupling between air temperature and surface energy fluxes associated with soil moisture variability, vegetation change, and human water/land management. However, existing studies on the coupling between hot extreme and surface energy fluxes are mainly based on the parameterized solution of climate model, which might not dynamically reflect all changes in the surface energy partitioning due to the effects of vegetation physiological control and human water/land management. In this study, for the first time, we used daily weather observations to identify hot spots where the daily hot extreme (i.e., the 99th percentile of maximum temperature, Tq99th) rises faster than local mean temperature (Tmean) during 1975–2017. Furthermore, we analyzed the relationship between the trends in temperature hot extreme relative to local average (ΔTq99th/ΔTmean) and the trends in evaporative fraction (ΔEF), i.e., the ratio of latent heat flux to surface available energy, using long-term latent and sensible heat fluxes which are informed by atmospheric boundary layer theory, machine learning, and ground-based observations of flux towers and weather stations. Hot spots of increase in ΔTq99th/ΔTmean are identified to be Europe, southwestern North America, Northeast Asia, and Southern Africa. The detected significant negative correlations between ΔEF and ΔTq99th/ΔTmean suggested that the hotspot regions are typically affected by annual/summer surface dryness. Our observation-driven findings have great implications in providing realistic observational evidences for the extreme climate change accelerated by surface energy partitioning.

Sensitivity of land–atmosphere coupling strength to changing atmospheric temperature and moisture over Europe

Abstract. The quantification of land–atmosphere coupling strength is still challenging, particularly in the atmospheric segment of the local coupling process chain. This is in part caused by a lack of spatially comprehensive observations of atmospheric temperature and specific humidity which form the verification basis for the common process-based coupling metrics. In this study, we aim at investigating where uncertainty in the atmospheric temperature and moisture affects the land–atmosphere coupling strength over Europe, and how changes in the mean temperature and moisture, as well as their vertical gradients, influence the coupling. For this purpose, we implemented systematic a posteriori modifications to the temperature and moisture fields from a regional climate simulation to create a spread in the atmospheric conditions. Afterwards, the process-based coupling metric convective triggering potential – low-level humidity index framework was applied to each modification case. Comparing all modification cases to the unmodified control case revealed that a strong coupling hotspot region in northeastern Europe was insensitive to temperature and moisture changes, although the number of potential coupling days varied by up to 20 d per summer season. The predominance of positive feedbacks remained unchanged in the northern part of the hotspot, and none of the modifications changed the frequent inhibition of feedbacks due to dry conditions in the atmosphere over the Mediterranean and the Iberian Peninsula. However, in the southern hotspot region in the north of the Black Sea, the dominant coupling class frequently switched between wet soil advantage and transition zone. Thus, both the coupling strength and the predominant sign of feedbacks were sensitive to changes in temperature and moisture in this region. This implies not only uncertainty in the quantification of land–atmosphere coupling strength but also the potential that climate-change-induced temperature and moisture changes considerably impact the climate there, because they also change the predominant atmospheric response to land surface wetness.

Improving the ESA CCI daily soil moisture time series with physically-based land surface model datasets using a Fourier time-filtering method

Abstract Models have historically been the source of global soil moisture (SM) analyses and estimates of land-atmosphere coupling, even though they are usually calibrated and validated only locally. Satellite-based analyses have grown in fidelity and duration, offering an independent observationally-based alternative. However, satellite-retrieved SM time series include random and periodic errors that degrade estimates of land-atmosphere coupling, including correlations with other variables. This study proposes a mathematical approach to adjust daily time series of the European Space Agency (ESA) Climate Change Initiative (CCI) satellite SM product using information from physical-based land surface model (LSM) datasets using a Fourier transform time-filtering method to match the temporal power spectra locally to the LSMs, which tend to agree well with in situ observations. When the original and time-filtered SM products are evaluated against ground-based SM measurements over the conterminous U.S., Europe, and Australia, results show the filtered SM has significantly improved subseasonal variability. The skill of the time-filtered SM is increased in temporal correlation by ∼0.05 over all analysis domains without introducing spurious regional patterns, affirming the stochastic nature of noise in satellite estimates, and skill improvement is found for nearly all land cover classes, especially savannas and grassland. Autocorrelation-based soil moisture memory (SMM), and the derived random component of soil moisture error (SME) are used to investigate the improvement of SM features. Time filtering reduces the random noise from the satellite-based SM product that is not explainable by physically-based SM dynamics; SME is usually diminished and the increased SMM is generally statistically significant.

Effects of ozone–vegetation interactions on meteorology and air quality in China using a two-way coupled land–atmosphere model

Abstract. Tropospheric ozone (O3) is one of the most important air pollutants in China and is projected to continue to increase in the near future. O3 and vegetation closely interact with each other and such interactions may not only affect plant physiology (e.g., stomatal conductance and photosynthesis) but also influence the overlying meteorology and air quality through modifying leaf stomatal behaviors. Previous studies have highlighted China as a hotspot in terms of O3 pollution and O3 damage to vegetation. Yet, few studies have investigated the effects of O3–vegetation interactions on meteorology and air quality in China, especially in the light of recent severe O3 pollution. In this study, a two-way coupled land–atmosphere model was applied to simulate O3 damage to vegetation and the subsequent effects on meteorology and air quality in China. Our results reveal that O3 causes up to 16 % enhancement in stomatal resistance, whereby large increases are found in the Henan, Hebei, and Shandong provinces. O3 damage causes more than 0.6 µmol CO2 m−2 s−1 reductions in photosynthesis rate and at least 0.4 and 0.8 g C m−2 d−1 decreases in leaf area index (LAI) and gross primary production (GPP), respectively, and hotspot areas appear in the northeastern and southern China. The associated reduction in transpiration causes a 5–30 W m−2 decrease (increase) in latent heat (sensible heat) flux, which induces a 3 % reduction in surface relative humidity, 0.2–0.8 K increase in surface air temperature, and 40–120 m increase in boundary-layer height in China. We also found that the meteorological changes further induce a 2–6 ppb increase in O3 concentration in northern and south-central China mainly due to enhanced isoprene emission following increased air temperature, demonstrating that O3–vegetation interactions can lead to strong positive feedback that can amplify O3 pollution in China. Our findings emphasize the importance of considering the effects of O3 damage and O3–vegetation interactions in air quality simulations, with ramifications for both air quality and forest management.

Land transpiration-evaporation partitioning errors responsible for modeled summertime warm bias in the central United States

Earth system models (ESMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) experiment exhibit a well-known summertime warm bias in mid-latitude land regions – most notably in the central contiguous United States (CUS). The dominant source of this bias is still under debate. Using validated datasets and both coupled and off-line modeling, we find that the CUS summertime warm bias is driven by the incorrect partitioning of evapotranspiration (ET) into its canopy transpiration and soil evaporation components. Specifically, CMIP6 ESMs do not effectively use available rootzone soil moisture for summertime transpiration and instead rely excessively on shallow soil and canopy-intercepted water storage to supply ET. As such, expected summertime precipitation deficits in CUS induce a negative ET bias into CMIP6 ESMs and a corresponding positive temperature bias via local land-atmosphere coupling. This tendency potentially biases CMIP6 projections of regional water stress and summertime air temperature variability under elevated CO2 conditions.

Effects of the surface coupling strength in the WRF/Noah-MP model on regional climate simulations over China

Land–atmosphere energy and moisture exchange can strongly influence local and regional climates. However, high uncertainty exists in the representation of land–atmosphere interactions in numerical models. Parameterization of surface exchange processes is greatly affected by parameter Czil, which, however, is typically defined as a domain-wide constant value. In this study, we examine the sensitivity of regional climate simulations over China to different surface exchange strengths via three Czil schemes (default (without Czil), constant (Czil = 0.1), and dynamic canopy-height-dependent Czil-h schemes) within a 13-km-resolution Weather Research and Forecasting model coupled with the Noah land surface model with multiparameterization options (WRF/Noah-MP). Our results demonstrate that compared to the other two schemes, the Czil-h scheme substantially reduces land–atmosphere coupling strength overestimation, and comparison to Chinese terrestrial ecosystem flux research network (ChinaFLUX) observations reveals the capability of the Czil-h scheme to better match observed surface energy and water variations. The results of the application of the various Czil schemes in four typical climate zones in China demonstrate that the Czil-h simulations achieve the closest agreement with field observations. The Czil-h scheme can narrow the positive discrepancies in the simulated precipitation and surface fluxes and the negative biases of the land surface temperature in Northeast China, North China, eastern Northwest China, and Southwest China. In particular, the above remarkable improvements produced by the Czil-h scheme primarily occur in areas covered with short vegetation. Additionally, the precipitation simulated with the Czil-h scheme exhibits more intricate and uncertain changes compared with surface flux simulations due to the nonlocal impacts of the surface exchange strength resulting from atmospheric fluidity. Overall, our findings highlight the applicability of the dynamic Czil scheme as a better physical alternative to the current treatment of surface exchange processes in atmosphere coupling models.

Subseasonal prediction performance for South American land–atmosphere coupling in extended austral summer

AbstractLand–atmosphere feedbacks, through water and energy exchanges, provide subseasonal‐to‐seasonal predictability of the hydrological cycle. We analyse subseasonal land–atmosphere coupling over South America (SA) during extended austral summer for the soil moisture‐to‐precipitation and soil moisture‐to‐air temperature feedback pathways. We evaluate subseasonal hindcasts from global forecasting systems from the UK Met Office, the National Centers for Environmental Prediction (NCEP), the European Centre for Medium Range Weather Forecasts and the Center for Weather Forecast and Climate Studies (CPTEC), for the common period of 1999–2010, against two reanalyses. Biases in land–atmosphere states are established in the first week of hindcasts and increase with lead time. By Week 5, all the models only demonstrate good performance over northern, northeastern and southeastern SA for soil moisture and evapotranspiration and over tropical and subtropical SA for temperature. The hindcasts show stronger coupling at longer lead–lag between variables than reanalyses. Our results highlight possible deficiencies in feedbacks between soil moisture and precipitation in CPTEC and NCEP forecasts over the Amazon due to initial dry soil moisture biases, and in feedbacks between soil moisture and temperature for all four investigated models over southeastern SA due to erroneous representations of evapotranspiration.

Soil moisture as an essential component for delineating and forecasting agricultural rather than meteorological drought

Drought is a recurring, complex, and extreme climatic phenomenon characterized by subnormal precipitation for months to years triggering negative impacts on agriculture, energy, tourism, recreation, and transportation sectors. Agricultural drought assessment is based on a deficit of soil moisture (SM) during the plant-growing season, whereas meteorological drought corresponds to subnormal precipitation over months to years. However, satellite-derived agricultural and meteorological drought indices (including those comprising root-zone SM) have not been comprehensively compared to evaluate their ability for drought delineation and particularly forecasting across climate regimes, land cover and soil types, and irrigation management (irrigated vs. rainfed) in the contiguous USA (CONUS). Here, we did so from 2015 to 2019 within the CONUS. In most regions except the US Midwest and Southeast, SM-based indices (e.g., Palmer Z, SMAP, SWDI) delineated agricultural drought better than meteorological (e.g., SPI, SPEI) and hybrid (Comprehensive Drought Index, CDI) drought indices. In contrast, the SPI and SPEI showed strong correlation with the aridity index in most part of the CONUS except the Midwest. SM-based and hybrid indices also demonstrated skills for agricultural drought forecasting (represented by end-of-year cumulative GPP), predominantly in the early growing season and particularly in irrigated rather than rainfed croplands. These findings indicate the leading role of SM in controlling ecosystem dryness and confirm “drought memory”, possibly due to SM-memory in land-atmosphere coupling. Proper application of meteorological and agricultural drought indices and their contrasting spatial-temporal controls on plant growth and ecosystem dryness has the potential to improve our understanding of drought evolution and provide early drought forecasting across large regions with diverse climate regimes, land cover types, soil textural classes, and irrigation management.

Soil Moisture-Boundary Layer Feedbacks on the Loess Plateau in China Using Radiosonde Data with 1-D Atmospheric Boundary Layer Model

The Loess Plateau is one land-atmosphere coupling hotspot. Soil moisture has an influence on atmospheric boundary layer development under specific early-morning atmospheric thermodynamic structures. This paper investigates the sensitivity of atmospheric convection to soil moisture conditions over the Loess Plateau in China by using the convective triggering potential (CTP)—humidity index (HIlow) framework. The CTP indicates atmospheric stability and the HIlow indicates atmospheric humidity in the low-level atmosphere. By comparing the model outcomes with the observations, the one-dimensional model achieves realistic daily behavior of the radiation and surface heat fluxes and the mixed layer properties with appropriate modifications. New CTP-HIlow thresholds for soil moisture-atmosphere feedbacks are found in the Loess Plateau area. By applying the new thresholds with long-time scales sounding data, we conclude that negative feedback is dominant in the north and west portion of the Loess Plateau; positive feedback is predominant in the south and east portion. In general, this framework has predictive significance for the impact of soil moisture on precipitation. By using this new CTP-HIlow framework, we can determine under what atmospheric conditions soil moisture can affect the triggering of precipitation and under what atmospheric conditions soil moisture has no influence on the triggering of precipitation.

Increasing the Depth of a Land Surface Model. Part I: Impacts on the Subsurface Thermal Regime and Energy Storage

Abstract The representation of the thermal and hydrological states in land surface models is important for a realistic simulation of land–atmosphere coupling processes. The available evidence indicates that the simulation of subsurface thermodynamics in Earth system models is inaccurate due to a zero-heat-flux bottom boundary condition being imposed too close to the surface. To assess the influence of soil model depth on the simulated terrestrial energy and subsurface thermal state, sensitivity experiments have been carried out in piControl, historical, and RCP scenarios. A deeper bottom boundary condition placement has been introduced into the JSBACH land surface model by enlarging the vertical stratification from 5 to 12 layers, thereby expanding its depth from 9.83 to 1416.84 m. The model takes several hundred years to reach an equilibrium state in stand-alone piControl simulations. A depth of 100 m is necessary, and 300 m recommendable, to handle the warming trends in historical and scenario simulations. Using a deep bottom boundary, warming of the soil column is reduced by 0.5 to 1.5 K in scenario simulations over most land areas, with the largest changes occurring in northern high latitudes, consistent with polar amplification. Energy storage is 3–5 times larger in the deep than in the shallow model and increases progressively with additional soil layers until the model depth reaches about 200 m. While the contents of Part I focus on the sensitivity of subsurface thermodynamics to enlarging the space for energy, Part II addresses the sensitivity to changing the space for water and improving hydrological and phase-change interactions.

Linkage between anomalies of pre-summer thawing of frozen soil over the Tibetan Plateau and summer precipitation in East Asia

The Tibetan Plateau (TP) is sensitive to climate change in the land–atmosphere coupling mechanism due to its complex topographic features and unique geographic location. This study explored the teleconnection between pre-summer thawing of frozen soil over the TP and summer precipitation in East Asia in the Meiyu–Baiu rainy season (June, July) from 1981 to 2019 using maximum covariance analysis (MCA). The precipitation fields forced by thawing of frozen soil were calculated by the coupled manifold technique. The variations in East Asian precipitation are significantly impacted by thawing of frozen soil over the TP, with a variance explained ratio in the surrounding middle and lower reaches of Yangtze River (MLYR) of around 10%–20%. The MCA analysis also revealed that the thickness of pre-summer frozen soil thawing had a positive relationship with summer precipitation in the MLYR and southern Japan (fraction = 0.59, correlation ≈ 0.99). To find out the possible mechanism, composite analyses were conducted on atmospheric and surface components with reanalysis products. The analysis results suggested that more (less) frozen soil thawing would increase (decrease) the sensible heat and land surface temperature with enhanced (weakened) surface diabatic heating over the TP. Then, the positive (negative) surface diabatic heating would result in an enhanced (weakened) South Asia High extending eastward followed by stronger (weaker) upper troposphere (200 hPa) westerlies, as well as the West North Pacific Subtropical High extending westward. As a result, the northeastward movement of the integrated vapor transport intensified (weakened) with the westward extension (eastward retreat) of the Meiyu–Baiu rain belt, leading to more (less) precipitation in the MLYR and southern Japan. It is thus suggested that pre-summer thawing of frozen soil over the TP could play a vital role in regulating East Asian summer precipitation and movement of the Meiyu–Baiu rain belt.

Paleoclimate-conditioning reveals a North Africa land–atmosphere tipping point

While paleoclimate records show that the Earth System is characterized by several different tipping points, their representation within Earth System models (ESMs) remains poorly constrained. This is because historical observations do not encompass variations large enough to provoke such regime changes, and paleoclimate conditions are rarely used to help develop and tune ESMs, which potentially ignores a rich source of information on abrupt climate change. A critical example is the early to mid-Holocene "greening" and subsequent rapid desertification of the Sahara, which most ESMs fail to reproduce, casting doubt on the representation of land-atmosphere coupling and monsoon dynamics. Here, we show that this greening and abrupt termination can be successfully simulated with one ESM after optimizing uncertain model components using both present-day observations and crucially mid-Holocene (6,000 y before present) reconstructions. The optimized model displays abrupt threshold behavior, which shows excellent agreement with long paleoclimate records that were not used in the original optimization. These results suggest that in order to realistically capture climate-system thresholds, ESMs first need to be conditioned with appropriate paleoclimate information.

Emergent constraints on tropical atmospheric aridity—carbon feedbacks and the future of carbon sequestration

Carbon-climate feedbacks, which amplifies or attenuates atmospheric CO2 from fossil fuel emissions, are one of the largest sources of uncertainty in climate projections. However, these feedbacks depend both on temperature and its coupling to water and energy cycles, especially in the tropics. We show that atmospheric aridity—quantified as vapor pressure deficit (VPD)—is a good proxy for this coupling. Tropical VPD is strongly correlated to the global CO2 growth rate (CGR) with observed present-day sensitivities of −2.5 ± 0.4 GtC mb−1 yr−1. The sensitivity of CGR to tropical VPD interannual variability has increased by a factor of 1.7 ± 0.3 in the 21st century. A combination of causality and statistical analysis point to mechanistic moisture drivers of the VPD–CGR sensitivities, independent of temperature. Observational records provide evidence that tropical atmospheric aridity is linked to both water deficit and spatially correlated with evaporative fraction suggesting that CGR variability is indirectly driven by land—atmosphere coupling (compound soil and atmospheric drought). This coupling is manifest as a kind of carbon-climate feedback in CMIP6 Earth System Models where long–term increases in tropical VPD reduce tropical carbon storage but with a substantial inter-model range [−1.4 to −59.4 GtC mb−1]. However, by employing a hierarchical emergent constraint, the best estimate of atmospheric aridity—carbon cycle feedback (φ TL ) is 10 GtC mb−1, which is 28% lower than model estimates with an uncertainty reduction of 50%. Our results bridge the role of moisture and land–atmosphere coupling on net carbon variability to the vulnerability of carbon storage in a changing climate.

Detection, Attribution, and Future Response of Global Soil Moisture in Summer

Soil moisture variations and its relevant feedbacks (e.g., soil moisture–temperature and soil moisture–precipitation) have a crucial impact on the climate system. This study uses reanalysis and Coupled Model Intercomparison Project phase 6 simulations datasets to detect, attribute, and project soil moisture variations. The effect of anthropogenic forcings [greenhouse gases (GHG), anthropogenic aerosols (AA), and land use (LU) change] on soil moisture is much larger than that of the natural forcing. Soil moisture shows a drying trend at a global scale, which is mainly attributed to GHG forcing. The effects of external forcings vary with the regions significantly. Over eastern South America, GHG, AA, and natural forcings make soil dry, while LU forcing makes the soil wet. Over severely drying Europe, all the external forcings including GHG, AA, LU, and natural forcing exhibit drying effect. The optimal fingerprint method detection results show that some of GHG, AA, LU, and natural signals can be detected in soil moisture variations in some regions such as Europe. The soil will keep drying in all scenarios over most parts of the globe except Sahel and parts of mid-latitudes of Asia. With the increase of anthropogenic emissions, the variation of global soil moisture will be more extreme, especially in hotspots where the land–atmosphere coupling is intensive. The drying trend of soil moisture will be much larger on the surface than in middle and deep layers in the future, and this phenomenon will be more severe under the high-emission scenario. It may be affected by increased evaporation and the effect of carbon dioxide fertilization caused by global warming.