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The effect of soil moisture (SM) on precipitation is an important issue in the land–atmosphere interaction and shows largely regional differences. In this study, the SM of the ERA-Interim reanalysis and precipitation data of the weather stations were used to investigate their relationship over eastern China during July and August. Moreover, the WRF model was applied to further validate the effect of SM on rainfall. In the observations, a significantly negative relationship was found that, when the soil over southern China is wet (dry) in July, the rainfall decreases (increases) over the Huang–Huai–River basin (hereafter HHR) in August. In the model results, the soil can “memorize” its wet anomaly over southern China from July to August. In August, the wet soil increases the latent heat flux at surface and the air moisture at lower levels of the atmosphere, which is generally unstable due to the summer monsoon. Thus, upward motion is prevailing over southern China in August, and the increased surface air moisture is transported upwards. After that, the condensation of water vapor is enhanced at the middle and upper levels, increasing the release of latent heat in the atmosphere. The heat release forms a cyclonic circulation at the lower levels over eastern China, and induces the transport and convergence of water vapor increased over southern China in August. This further strengthens the upward motion over southern China and the cyclonic circulation at the lower levels. Therefore, positive feedback appears between water vapor transport and atmospheric circulation. Meanwhile, the cyclonic circulation over southern China results in a response of water vapor divergence and a downward motion over HHR. Consequently, the negative anomalies of precipitation occur over HHR in August. When the July soil is dry over southern China, the opposite results can be found through the similar mechanism.

Previous studies suggested that the dry–wet surface state over the Indo-China Peninsula (ICP), closely associated with the local spring precipitation, is an important seasonal predictor for the East Asian summer monsoon and extreme climate. Hence, this work investigates the inter-annual variability of spring precipitation over the ICP and its relationship with El Nino-Southern Oscillation (ENSO) during 1958–2019. The results show that the spring precipitation anomalies over the ICP are highly linked to the ENSO-induced atmospheric circulation anomalies. In particular, there are large asymmetries in the precipitation anomalies for the spring following ENSO. During the decaying spring of the El Nino events, the precipitation decrease mainly occurs over the Western ICP associated with an anomalous low-level anticyclone over the western North Pacific. In contrast, during the decaying spring of the La Nina events, a stronger precipitation increase broadly extends into the Southeastern ICP. This is owing to a nonlinear effect of ENSO on the atmospheric circulation. Compared to El Nino, the abnormal center of La Nina extends too far westwards, inducing a westward movement of the anomalous atmospheric circulation, which results in a stronger effect on the spring ICP precipitation. Our findings emphasize the nonlinear responses of the spring ICP precipitation to ENSO. This has important implications for the seasonal climate prediction over the ICP, especially for the Southeastern ICP countries/regions.

This study investigated the relationship between the pre-summer soil moisture (SM) over the Indo-China Peninsula (ICP) and the summer precipitation in the middle and lower reaches of the Yangtze River basin (MLR-YRB) under different SST backgrounds. When spring soil over the ICP was dry (wet), summer precipitation in most parts of the MLR-YRB was significantly higher (lower) under normal SST backgrounds (in the inner-quartile years). However, the SM anomalies produced a limited influence on summer precipitation under strong SST conditions (in the outer-quartile years). Further analysis indicates that in the inner-quartile years, the thermal heating anomalies induced by abnormal SM evidently increased (decreased) the geopotential heights above the western tropical Pacific, and enhanced (weakened) the low-level anticyclonic circulation, thereby increasing (decreasing) the warm and moist southerly flow from the ICP and western Pacific regions to the MLR-YRB, which was conducive to more (less) precipitation in the MLR-YRB. In the outer-quartile years, thermal anomalies of the ICP had very weak effects on both the wind fields above the western Pacific and the water vapor flux to the MLR-YRB, thereby leading to a limited increase (decrease) in summer precipitation. The different responses of the local and surrounding atmospheric circulations to pre-summer SM anomalies over the ICP against different SST backgrounds produced distinct impacts on the distribution of summer precipitation in the MLR-YRB. The dry (wet) anomalies of spring SM in the ICP could also increase (decrease) the zonal thermal contrast between the ICP and the South China Sea (SCS) and impact the SCS summer monsoon differently in the inner-quartile and outer-quartile years. Based on numerical experiments with the Community Earth System Model (CESM), this work further confirmed the observational results and explored the physical mechanism by which the ICP spring soil moisture affects the MLR-YRB summer precipitation under various SST backgrounds. The simulation results show that a dry (wet) ICP spring soil significantly increased (decreased) the MLR-YRB rainfall in the following summer. This negative correlation was stronger in the inner-quartile years. The atmospheric response to changes in the surface thermal conditions was more significant in the inner-quartile years. This surface heating anomaly over the ICP led to anomalous southwest wind over the SCS, which could enhance the Asian summer monsoon and increase summer precipitation in the MLR-YRB.

Various surface soil moisture (SM) data from station observations, the Soil Moisture Active Passive (SMAP) mission, three reanalyses (ERA-Interim, CFSR, and NCEP RII), and the Global Land Data Assimilation System (GLDAS) are used to explore the sub-seasonal variations of SM (SSV-SM) over eastern China. Based on the correlation with SM of SMAP, reanalyses, and GLDAS, it is found that the variations of SM observed by Liuhe and Chunan stations can generally represent the SM variations over eastern China. The correlation coefficients between the SMAP and station SM are around 0.7. The SMAP product can well capture the time variation of SM over eastern China. The spectral analysis suggests that periodic variations of SM are mainly and significantly over the 10–30-day period over eastern China in all the data. The significant spectra over the 10–30-day period basically occur during the rainy season over eastern China. For the spatial aspect of SSV-SM, precipitation is the main factor causing the spatial distribution of SSV-SM over eastern China. However, the spectra of the station precipitation are not consistent with those of the station SM, and there is less coherence between the precipitation and SM over the periods during which SM has significant spectra. This indicates that SSV-SM is also affected by other factors.

The future vegetation–climate system over East Asia, as well as its dependence on Representative Concentration Pathways (RCPs), is investigated using a regional climate–vegetation model driven with boundary conditions from Flexible Global Ocean–Atmosphere–Land System Model: Grid-point Version 2. Over most of the region, due to the rising CO2 concentration and climate changes, the model projects greater vegetation density (leaf area index) and gradual shifts of vegetation type from bare ground to grass or from grass to trees; the projected spatial extent of the vegetation shift increases from RCP2.6 to RCP8.5. Abrupt shifts are projected under RCP8.5 over northeast China (with grass replacing boreal needleleaf evergreen trees due to heat stress) and India (with tropical deciduous trees replacing grass due to increased water availability). The impact of vegetation feedback on future precipitation is relatively weak, while its impact on temperature is more evident, especially during DJF over northeast China and India with differing mechanisms. In northeast China, the projected forest loss induces a cooling through increased albedo, and daytime high temperature (Tmax) is influenced more than nighttime low temperature (Tmin); in India, increased vegetation cover induces an evaporative cooling that outweighs the warming effect of an albedo decrease in DJF, leading to a weaker impact on Tmax than on Tmin. Based on a single model, the qualitative aspects of these results may hold while quantitative assessment will benefit from a follow-up regional model ensemble study driven by multiple general circulation models.

Soil moisture is a key variable of the land surface and its variations are an important issue in climate studies. In this study, we employed the Community Earth System Model (CESM) for 20 ensemble member simulations of the 50-year period from 1965 to 2014 and used canonical correlation analysis (CCA) and multiple regression to analyze the spatiotemporal characteristics of soil moisture predictability. The effects of soil moisture persistence and sea surface temperature (SST) as an external forcing on its predictability were analyzed. Results show that the soil moisture predictability due to its persistence is 1–2 months and considering SST as an external forcing can significantly increase its predictability. Regions that exhibit a significant increase in predictability are mainly tropical regions, North America and Western Asia during winter and spring. In tropical regions, SST increases the predictability of soil moisture by influencing the local surface temperature and precipitation. In other regions, the effects of SST on wind speed, cloud cover, and surface evaporation also contribute significantly to the increase in soil moisture predictability. The results were validated through the analysis based on soil moisture data from land data assimilation system and observed precipitation.

Land–atmosphere interaction plays an important role in regional weather and climate. Using the soil moisture (SM) data from the Global Land Data Assimilation System V2.0, the present study examines the land–atmosphere interaction during spring over the Indo-China Peninsula (ICP) and its effect on the following summer climate over the Yangtze River basin. The analyses show that the abnormal SM over the ICP in spring would significantly change the local surface air temperature by affecting the evapotranspiration. In particular, such a SM effect on the local air temperature can persist to the following summer owing to a strong ICP SM memory, which can in turn influence the East Asian summer monsoon as well as the remote precipitation and temperature over the Yangtze River basin. The persistent abnormally lower (higher) SM over the ICP induces less (more) local evapotranspiration, increasing (decreasing) the surface temperature. The resultant anomalous heating (cooling) over the ICP raises (lowers) the local geopotential height, which attracts (repels) the Western Pacific Subtropical High (WPSH) extending westward. Accompanied by an excessive westward extension of the summer WPSH, an anomalously enhanced southwesterly wind would bring more moisture to the Yangtze River basin at the lower troposphere. This situation intensifies the Meiyu front and precipitation over the Yangtze River basin. Further thermodynamic and dynamic analyses support that the monsoonal circulation anomalies associated with the westward extension of the WPSH mainly contribute to the summer precipitation anomalies over the Yangtze River basin. In addition, more precipitation accompanied with more cloud cover and less downward solar radiation that reduce the local air temperature, and vice versa. This highlights that the spring SM over the ICP is an important predictor for the following summer climate over the East Asia. The implication for predicting extreme weather events in summer over the Yangtze River basin is also discussed.

Understanding the rainfall climatology and variability over Central Equatorial Africa (CEA) has largely been hampered by the lack of adequate in situ observations and meteorological stations for the last three decades. Large differences and uncertainties among several observational and reanalysis data sets and various climate model simulations present another big challenge. This study comprehensively assesses the currently widely used reanalysis products based on quality-controlled radiosonde observations and a new gauge-based rainfall data set, NIC131, in order to identify the “best” reanalysis products available over CEA. Among the seven reanalysis data sets (i.e., 20CR, CFSR, ERA-Interim, JRA-55, MERRA2, NCEP-1 and NCEP-2), MERRA2 is closest to NIC131 in reproducing the mean climatology and interannual variability and has the smallest biases and root-mean-square error (RMSE) in describing the observed wind fields in the lower- and middle-troposphere, and the two NCEP reanalyses can better capture geopotential height fields than the other reanalyses. Overall, the reanalyses capture the major features of the rainfall seasonal cycle and the seasonal evolution in the reference data but demonstrate an evident spread of spatiotemporal characteristics. By examining the moisture transport, we find that the differences in the lower- and middle-tropospheric circulation can reasonably explain the differences in the rainfall climatology among the reanalyses. Considering the large differences in horizontal and vertical wind fields among the seven reanalyses, we need to use the best reanalysis wind and moisture fields to explain the observed rainfall and associated circulation changes over CEA.

The reforecast of 11 models in the sub-seasonal to seasonal (S2S) prediction project has been analyzed to investigate the effects of the Madden–Julian Oscillation (MJO) on the prediction skill of winter 2-m air temperature (T2M) over China. Most of the S2S models have useful prediction skills (correlation skill ≥ 0.5) before pentads 3 and 4. ECMWF model can possess a good prediction skill for almost four pentads and perform the best among the 11 models. ECCC and ECMWF models have more reliable ensemble prediction and better ensemble strategies than the other models. All the models tend to have lower T2M prediction skill over the Tibetan Plateau than that over the other regions of China. Moreover, initial state and model resolution have important influences on S2S prediction skill. In most of the models at pentads 3 and 4, T2M prediction skill of forecast with MJO at initial time is significantly higher than that without over parts of China. However, the spatial distributions of the prediction skill differences due to MJO are not consistent among the 11 models. This indicates that there is an uncertainty of the effects of MJO on T2M prediction over China at pentads 3 and 4. Planetary-scale teleconnection pattern excited by MJO over the Northern Hemisphere is the possible reason for the effect of MJO on T2M prediction skill. Because most of the models can maintain this teleconnection pattern for 3–4 forecast pentads, MJO can affect the atmospheric circulation over China during this period, and improve the T2M prediction skill in the models. This finding suggests that the prediction of winter T2M over China initialized with MJO can be more skillful at pentads 3 and 4 than that without MJO in the initial conditions.

Previous studies show that Indo-Pacific sea surface temperature (SST) variations may help to explain the observed long-term drought during April–May–June (AMJ) since the 1990s over Central equatorial Africa (CEA). However, the underlying physical mechanisms for this drought are still not clear due to observation limitations. Here we use the AMIP-type simulations with 24 ensemble members forced by observed SSTs from the ECHAM4.5 model to explore the likely physical processes that determine the rainfall variations over CEA. We not only examine the ensemble mean (EM), but also compare the “good” and “poor” ensemble members to understand the intra-ensemble variability. In general, EM and the “good” ensemble member can simulate the drought and associated reduced vertical velocity and anomalous anti-cyclonic circulation in the lower troposphere. However, the “poor” ensemble members cannot simulate the drought and associated circulation patterns. These contrasts indicate that the drought is tightly associated with the tropical Walker circulation and atmospheric teleconnection patterns. If the observational circulation patterns cannot be reproduced, the CEA drought will not be captured. Despite the large intra-ensemble spread, the model simulations indicate an essential role of SST forcing in causing the drought. These results suggest that the long-term drought may result from tropical Indo-Pacific SST variations associated with the enhanced and westward extended tropical Walker circulation.

Under the exacerbation of climate change, climate extreme events, especially for drought, happened frequently and intensively across the globe with greater spatial differences. We used the Standardized Precipitation-Evapotranspiration Index computed from the routine meteorological observations at 269 sites in Southwest China (SWC) to study the drought characteristics (e.g., extent, duration and intensity) and their decadal variations during 1971–2012. It was revealed that the drought, in responses to the coupling between decadal precipitation and potential evapotranspiration (PET) anomalies, differed among regions and periods. For the entire SWC, droughts in 1970s and 2000s+ was generally stronger than in 1980s and 1990s with respect to their spatial extent, duration and intensity, especially in 2000s+. It was well-known that drought was closely related with a lack of precipitation; however, the impact of atmospheric demand of evaporation (reflected by PET here) on drought (e.g., duration and intensity) was rarely paid enough attentions. To that end, a spatial multi-linear regression approach was proposed in this study for quantifying the contributions of decadal PET and precipitation variations to drought duration and intensity. We have found that the contributions of decadal PET anomalies to drought duration and intensity could exceed those of precipitation, e.g., during 1980s and 1990s in SWC. Additionally, despite the strongest droughts in 2000s+, it was suggested that PET could exert comparable impacts on drought anomalies as precipitation. All these findings implied that PET plays a critical role in drought event, which acts to amplify drought duration and intensity. To sum up, this study stressed the need for enough attentions for PET processes in drought studies.

This study examined the global sea surface temperature (SST) predictions by a so-called multiple-ocean analysis ensemble (MAE) initialization method which was applied in the National Centers for Environmental Prediction (NCEP) Climate Forecast System Version 2 (CFSv2). Different from most operational climate prediction practices which are initialized by a specific ocean analysis system, the MAE method is based on multiple ocean analyses. In the paper, the MAE method was first justified by analyzing the ocean temperature variability in four ocean analyses which all are/were applied for operational climate predictions either at the European Centre for Medium-range Weather Forecasts or at NCEP. It was found that these systems exhibit substantial uncertainties in estimating the ocean states, especially at the deep layers. Further, a set of MAE hindcasts was conducted based on the four ocean analyses with CFSv2, starting from each April during 1982–2007. The MAE hindcasts were verified against a subset of hindcasts from the NCEP CFS Reanalysis and Reforecast (CFSRR) Project. Comparisons suggested that MAE shows better SST predictions than CFSRR over most regions where ocean dynamics plays a vital role in SST evolutions, such as the El Nino and Atlantic Nino regions. Furthermore, significant improvements were also found in summer precipitation predictions over the equatorial eastern Pacific and Atlantic oceans, for which the local SST prediction improvements should be responsible. The prediction improvements by MAE imply a problem for most current climate predictions which are based on a specific ocean analysis system. That is, their predictions would drift towards states biased by errors inherent in their ocean initialization system, and thus have large prediction errors. In contrast, MAE arguably has an advantage by sampling such structural uncertainties, and could efficiently cancel these errors out in their predictions.

Previous research found that the warming rate observed for the period 1979–2012 increases dramatically with decreasing vegetation greenness over land between 50°S and 50°N, with the strongest warming rate seen over the driest regions such as the Sahara desert and the Arabian Peninsula, suggesting warming amplification over deserts. To further this finding, this paper explores possible mechanisms for this amplification by analyzing observations, reanalysis data and historical simulations of global coupled atmosphere–ocean general circulation models. We examine various variables, related to surface radiative forcing, land surface properties, and surface energy and radiation budget, that control the warming patterns in terms of large-scale ecoregions. Our results indicate that desert amplification is likely attributable primarily to enhanced longwave radiative forcing associated with a stronger water vapor feedback over drier ecoregions in response to the positive global-scale greenhouse gas forcing. This warming amplification and associated downward longwave radiation at the surface are reproduced by historical simulations with anthropogenic and natural forcings, but are absent if only natural forcings are considered, pointing to new potential fingerprints of anthropogenic warming. These results suggest a fundamental pattern of global warming over land that depend on the dryness of ecosystems in mid- and low- latitudes, likely reflecting primarily the first order large-scale thermodynamic component of global warming linked to changes in the water and energy cycles over different ecosystems. This finding may have important implications in interpreting global warming patterns and assessing climate change impacts.

The summer rainfall in China has a large interannual variability, which results from the concurrent variations of precipitation frequency and intensity. Using the observed daily precipitation in the 194 stations during recent 62 years, we examine the relative importance of the frequency and intensity in the variability of the rainfall. A simple method, based on linear regression, is used to estimate the relative importance. The products of the change rates of rainfall with respect to frequency and intensity, determined from the regression, and the corresponding standard deviations of the two variables, which reflect their variation scales, are defined to measure the importance of frequency and intensity. To determine the frequency, rainfall amount, and intensity from daily precipitation, we need a threshold to define the “rainy day”. In this study, we use a series of thresholds, ranging from 1 to 30 mm/day. So, while presenting the result of relative importance for each threshold, we also examine how the relative importance varies with the threshold. Results show that for the threshold of 1 mm/day, with which the rainfall may include even the light rains, the variabilities of summer rainfall in most stations are dominated by intensity. With the increase in threshold, the importance of frequency increases, while the importance of intensity decreases. When the threshold reaches 30 mm/day, with which the rainfall includes only moderate-to-heavy rains, the variabilities of the rainfall in all stations are dominated by frequency. Analysis suggests that such a change, in the dominance with the threshold, is reasonable. This reasonability, in turn, supports the reliability and robustness of the method.

Impacts of large-scale urbanization over eastern China on East Asian summer monsoon circulation and climate are investigated by comparing three 25-year climate simulations with and without incorporating modified land cover maps reflecting two different idealized large-scale urbanization scenarios. The global atmospheric general circulation model CAM4.0 that includes an urban canopy parameterization scheme is employed in this study. The large-scale urbanization over eastern China leads to a significant warming over most of the expanded urban areas, characterized by an increase of 3 K for surface skin temperature, 2.25 K for surface air temperature, significant warming of both daily minimum and daily maximum air temperatures, and 0.4 K for the averaged urban–rural temperature difference. The urbanization is also accompanied by an increase in surface sensible heat flux, a decrease of the net surface shortwave and long-wave radiation, and an enhanced surface thermal heating to the atmosphere in most Eastern Asia areas. It is noted that the responses of the East Asian summer monsoon circulation exhibits an evident month-to-month variation. Across eastern China, the summer monsoon in early summer is strengthened by the large-scale urbanization, but weakened (intensified) over southern (northern) part of East Asia in late summer. Meanwhile, early summer precipitation is intensified in northern and northeastern China and suppressed in south of ~35°N, but late summer precipitation is evidently suppressed over northeast China, the Korean Peninsula and Japan with enhancements in southern China, the South China Sea, and the oceanic region south and southeast of the Taiwan Island. This study highlights the evidently distinct month-to-month responses of the monsoon system to the large-scale urbanization, which might be attributed to different basic states, internal feedbacks (cloud, rainfall) as well as a dynamic adjustment of the atmosphere. Further investigation is required to understand the dynamic mechanisms by which a large-scale urbanization in China affects eastern Asian climate and summer monsoon circulation, especially possible internal feedbacks relevant to the sub-seasonal changes of the monsoon system.

This study investigates the decadal variability of rainfall over China in boreal summer (June–August) since the early 1990s. Results show that the rainfall experiences an abrupt decadal change at around 2002/2003. The decadal change is statistically significant and characterized by an out-of-phase pattern between southern China (SC) and the Yellow-Huaihe River Valley (YHRV). The rainfall over SC decreases during the decade 2003–2012 compared to that in the preceding decade 1993–2002. A simultaneous decadal increase in rainfall has occurred over the YHRV. Meanwhile, a significant sea surface temperature warming appears over the western Pacific Ocean and the northern Indian Ocean after 2002 on the decadal time scale. Further analysis reveals that enhanced convections are activated over the tropical regions between 130°E and 160°E and west of 80°E due to the SST anomalies, which induce the dry air in an area of anomalous subsidence located over SC and the northern South China Sea (SCS) via zonal circulation. Accompanied by the anomalous descending flow over the northern SCS, tropical cyclone (TC) activities in the SCS also experience a concurrent decadal reduction. The decrease in landfall TCs contributes to the decadal decrease in SC rainfall since 2003. Corresponding to the anomalous descending motion that is dominant south of 30°N, an anomalous moist ascending flow develops over the YHRV at around 35°N. Meanwhile, the western Pacific subtropical high becomes stronger and extends further westward during 2003–2012, leading to enhanced moisture transport by the southwesterly in the northwestern flank of subtropical high. As a result, more precipitation occurs over the YHRV. The above analysis has revealed the physical–dynamical processes involved in the decadal changes in rainfall over China. The mechanisms behind the out-of-phase pattern of rainfall changes between SC and the YHRV that occurred at 2002/2003 are explored.

The article Assessing reanalysis data for understanding rainfall climatology and variability over Central Equatorial Africa, written by Wenjian Hua, Liming Zhou, Sharon E.

This paper analyzes seasonal and diurnal variations of MODerate resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) data at ~1.1 km for the period of 2003–2011 over a region in West-Central Texas, where four of the world’s largest wind farms are located. Seasonal anomalies are created from MODIS Terra (~10:30 a.m. and 10:30 p.m. local solar time) and Aqua (~1:30 a.m. and 1:30 p.m. local solar time) LSTs, and their spatiotemporal variability is analyzed by comparing the LST changes between wind farm pixels (WFPs) and nearby non wind farm pixels (NNWFPs) using different methods under different quality controls. Our analyses show consistently that there is a warming effect of 0.31–0.70 °C at nighttime for the nine-year period during which data was collected over WFPs relative to NNWFPs, in all seasons for both Terra and Aqua measurements, while the changes at daytime are much noisier. The nighttime warming effect is much larger in summer than winter and at ~10:30 p.m. than ~1:30 a.m. and hence the largest warming effect is observed at ~10:30 p.m. in summer. The spatial pattern and magnitude of this warming effect couple very well with the geographic distribution of wind turbines and such coupling is stronger at nighttime than daytime and in summer than winter. Together, these results suggest that the warming effect observed in MODIS over wind farms are very likely attributable to the development of wind farms. This inference is consistent with the increasing number of operational wind turbines with time during the study period, the diurnal and seasonal variations in the frequency of wind speed and direction distribution, and the changes in near-surface atmospheric boundary layer (ABL) conditions due to wind farm operations. The nocturnal ABL is typically stable and much thinner than the daytime ABL and hence the turbine enhanced vertical mixing produces a stronger nighttime effect. The stronger wind speed and the higher frequency of the wind speed within the optimal power generation range in summer than winter and at nighttime than daytime likely drives wind turbines to generate more electricity and turbulence and consequently results in the strongest warming effect at nighttime in summer. Similarly, the stronger wind speed and the higher frequency of optimal wind speed at ~10:30 p.m. than that at ~1:30 a.m. might help explain, to some extent, why the nighttime LST warming effect is slightly larger at ~10:30 p.m. than ~1:30 a.m. The nighttime warming effect seen in spring and fall are smaller than that in summer and can be explained similarly.

Accurate simulations of terrestrial carbon assimilation and canopy transpiration are needed for both climate modeling and vegetation dynamics. Coupled stomatal conductance and carbon assimilation (A − gs) models have been widely used as part of land surface parameterizations in climate models to describe the biogeophysical and biogeochemical roles of terrestrial vegetation. Differences in various A − gs schemes produce substantial differences in the estimation of carbon assimilation and canopy transpiration, as well as in other land–atmosphere fluxes. The terrestrial carbon assimilation and canopy transpiration simulated by two different representative A – gs schemes, a simple A–gs scheme adopted from the treatments of the NCAR model (Scheme I) and a two-big-leaf A – gs scheme newly developed by Dai et al. (J Clim 17:2281–2299, 2004) (Scheme II), are compared via some sensitivity experiments to investigate impacts of different A − gs schemes on the simulations. Major differences are found in the estimate of canopy carbon assimilation rate, canopy conductance and canopy transpiration between the two schemes, primarily due to differences in (a) functional forms used to estimate parameters for carbon assimilation sub-models, (b) co-limitation methods used to estimate carbon assimilation rate from the three limiting rates, and (c) leaf-to-canopy scaling schemes. On the whole, the differences in the scaling approach are the largest contributor to the simulation discrepancies, but the different methods of co-limitation of assimilation rate also impact the results. Except for a few biomes, the residual effects caused by the different parameter estimations in assimilation sub-models are relatively small. It is also noted that the two-leaf temperature scheme produces distinctly different sunlit and shaded leaf temperatures but has negligible impacts on the simulation of the carbon assimilation.