Search

The sensitivity of hourly precipitation to cumulus parameterization and radiation schemes is explored by using the tropical-belt configuration of the Weather Research and Forecasting (WRF) Model. The domain covers the entire tropical region from 45°S to 45°N with a grid spacing of about 45 km. A series of 5-yr simulations with four cumulus parameterization schemes [new Tiedtke (NT), Kain–Fritsch (KF), new SAS (NS), and Tiedtke (TK)] and two radiation schemes (RRTMG and CAM) are carried out. We focus on the frequencies of hourly precipitation above three thresholds (0.02 mm h−1 = light drizzle rate; 0.2 mm h−1 = moderate rate; and 2 mm h−1 = heavy rate) between the observed CMORPH products and simulations. The sensitivity is higher for precipitation frequency than amount, and frequency is dominated by the cumulus parameterization. Frequencies above the moderate rate are well reproduced, whereas frequencies above the other two rates present large deviations. No combination of physical schemes is found to perform best in reproducing the frequencies above all thresholds. Simulations using the NT and NS schemes show higher precipitation frequencies above the light drizzle rate and lower precipitation frequencies above the heavy rate than those simulations using the KF and TK schemes. Precipitation frequency is higher when reproduced by experiments using the RRTMG scheme than those using the CAM scheme, except for frequencies above the light rate over oceans. The overestimation of frequency is mainly caused by too-frequent convective rainfall. The results imply that the triggering based on the vertical velocity may increase the occurrence of a rain event and that CAPE-based closure may increase the heavy precipitation frequency in the cumulus parameterization.

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.

To evaluate and clarify the daily precipitation characteristics (i.e. amount, frequency and intensity) of the regional climate models (RCMs) in China, long-term simulations were carried out using RegCM4.5 and Weather Research and Forecasting model (WRF), which were nested within the European Centre for Medium-Range Weather Forecasts (ECMWF)’s 20th century reanalysis (ERA-20C) between 1901 and 2010. The 2 RCMs were initially run at a resolution of 50 km. Analyses mainly compared the model-simulated climatic means and interannual variations of precipitation characteristics with those of dense and high-quality station observations (STN) from 1961-2010. Both models satisfactorily reproduced the seasonal mean precipitation amount, but they overestimated its frequency and underestimated its intensity. Extreme rainfall frequency was also underestimated by both RCMs. In winter (DJF), the interannual variabilities in dry days, light precipitation and moderate precipitation were well represented by both models. However, they poorly reproduced the counterparts of extreme precipitation in winter. In summer (JJA), the 2 RCMs performed well in simulating the interannual variability of extreme precipitation. Comparably, RegCM outperformed WRF in reproducing the spatial patterns of precipitation amount, interannual variations in extreme precipitation and rain events. By contrast, WRF better represented precipitation frequency in different sub-regions overall. Moreover, when the horizontal resolution of RegCM was increased from 50 to 25 km, there was a slight improvement in the representation of precipitation amount and intensity. Our results show that RCMs perform well in reproducing actual climatic means and interannual variations of daily precipitation characteristics in China, and that high-resolution RCM simulations can produce improved results for precipitation amount and intensity.

Accurate subseasonal-to-seasonal (S2S) atmospheric forecasts and hydrological forecasts have considerable socioeconomic value. This study conducts a multimodel comparison of the Tibetan Plateau snow cover (TPSC) prediction skill using three models (ECMWF, NCEP and CMA) selected from the S2S project database to understand their performance in capturing TPSC variability during wintertime. S2S models can skillfully forecast TPSC within a lead time of 2 weeks but show limited skill beyond 3 weeks. Compared with the observational snow cover analysis, all three models tend to overestimate the area of TPSC. Another remarkable issue regarding the TPSC forecast is the increasing TPSC with forecast lead time, which further increases the systematic positive biases of TPSC in the S2S models at longer forecast lead times. All three S2S models consistently exaggerate the precipitation over the Tibetan Plateau. The exaggeration of precipitation is prominent and always exists throughout the model integration. Systematic bias of TPSC therefore occurs and accumulates with the model integration time. Such systematic biases of TPSC influence the forecasted surface air temperature in the S2S models. The surface air temperature over the Tibetan Plateau becomes colder with increasing forecast lead time in the S2S models. Numerical experiments further confirm the causality.

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.

The middle and lower reaches of the Yangtze River valley (YRV), which are among the most densely populated regions in China, are subject to frequent flooding. In this study, the predictor importance analysis model was used to sort and select predictors, and five methods (multiple linear regression (MLR), decision tree (DT), random forest (RF), backpropagation neural network (BPNN), and convolutional neural network (CNN)) were used to predict the interannual variation of summer precipitation over the middle and lower reaches of the YRV. Predictions from eight climate models were used for comparison. Of the five tested methods, RF demonstrated the best predictive skill. Starting the RF prediction in December, when its prediction skill was highest, the 70-year correlation coefficient from cross validation of average predictions was 0.473. Using the same five predictors in December 2019, the RF model successfully predicted the YRV wet anomaly in summer 2020, although it had weaker amplitude. It was found that the enhanced warm pool area in the Indian Ocean was the most important causal factor. The BPNN and CNN methods demonstrated the poorest performance. The RF, DT, and climate models all showed higher prediction skills when the predictions start in winter than in early spring, and the RF, DT, and MLR methods all showed better prediction skills than the numerical climate models. Lack of training data was a factor that limited the performance of the machine learning methods. Future studies should use deep learning methods to take full advantage of the potential of ocean, land, sea ice, and other factors for more accurate climate predictions.

International audience; The Indian summer monsoon (ISM) is one of the world's strongest monsoon systems that brings about eighty percent of the annual rainfall to the Indian subcontinent and impacts the livelihood of more than a quarter of the world's population. Meanwhile, Asia is the world's second largest dust source-with major deserts in the Middle East, Central and East Asia. The interactions between the Asian dust and the ISM have received increasing attention in recent decades. Dust particles can modulate the circulation and precipitation of the ISM through absorption of solar and terrestrial radiation when suspending in the atmosphere and when deposited in snow and ice at surface and by acting as nuclei of liquid and ice clouds. In turn, the ISM can affect dust emissions, transport, and deposition through atmospheric circulation and wet scavenging. This review provides a) an overview of several physical mechanisms behind the interactions between the ISM rainfall and the Asian dust particularly the Middle East dust, b) a new hypothesis to explain the observed positive correlation between the Middle East dust and the ISM rainfall, and c) a summary of current progress and challenges in dust simulation in climate models. Finally, we propose future research directions aimed at improving dust-monsoon simulations in terms of dust long-term variability, absorbing property, and anthropogenic contributions.

Through a series of model simulations with an atmospheric general circulation model coupled to three different land surface models, this study investigates the impacts of land model ensembles and coupled model ensemble on precipitation simulation. It is found that coupling an ensemble of land models to an atmospheric model has a very minor impact on the improvement of precipitation climatology and variability, but a simple ensemble average of the precipitation from three individually coupled land-atmosphere models produces better results, especially for precipitation variability. The generally weak impact of land processes on precipitation should be the main reason that the land model ensembles do not improve precipitation simulation. However, if there are big biases in the land surface model or land surface data set, correcting them could improve the simulated climate, especially for well-constrained regional climate simulations.

North China is one of the most densely populated regions in the world. To its west, north, and northwest, the world's largest afforestation project has been going on for decades. At the same time, North China has been suffering from air pollution because of its large fossil fuel consumption. Here we show that the changes in land cover and aerosol concentration are coupled with the variations of land surface temperature, cloud cover, and surface solar radiation during the summer 2000–2013. Model experiments show that the interannual variation of aerosol concentration in North China is mainly a result of the varying atmospheric circulation. The increasing vegetation cover due to afforestation has enhanced surface evapotranspiration (ET) and cooled the local surface, and precipitation is observed to be increasing with ET. The model with prescribed increasing vegetation cover can simulate the increasing ET but cannot reproduce the increasing precipitation. Although this may be caused by model biases, the lack of aerosol processes in the model could also be a potential cause.

Groundwater extraction has grown tremendously in Saudi Arabia to meet the irrigation water demand since the 1980s, and irrigation is one of the major anthropogenic factors modulating regional hydroclimate. However, the link between irrigation and hydroclimate is not well understood in a dry region such as Saudi Arabia. In this study, we utilize three different regional climate models to explore the physical mechanisms behind the irrigation impacts in this region. The results are robust across models and show that when irrigation is applied, wetter soil results in higher evapotranspiration and cools the lower atmosphere, leading to an anomalous pressure field and alters vapor transportation. Precipitation decreases locally because of the local cooling effect, whereas additional water vapor convergence enhances precipitation west to the irrigated region. This west–east contrast of precipitation change indicates a possible link between irrigation expansion in the 1980s and subsequent decadal precipitation variations in central Saudi Arabia. We further find from observations a decadal west–east contrast of precipitation changes in Saudi Arabia to support the similar finding in the models. This study implies the importance of including anthropogenic water management in climate models and provides a better understanding of how irrigation impacts local-to-regional climate.

Since winter 2011, a record-breaking drought has occurred in California. Studies found that the drought is mainly caused by a persistent high-pressure system off the U.S. West Coast, which is linked to Pacific sea surface temperature anomalies. The water cycles associated with the droughts and floods are still not clearly understood. Here we show that the atmospheric circulation off the West Coast not only controls the atmospheric convergence and formation of precipitation but also largely determines surface wind speed, which further affects the evaporation over the eastern North Pacific, the major evaporative moisture source for California precipitation. Because of this mechanism, the ocean evaporation over the eastern North Pacific has been reduced during the recent drought. However, the ocean evaporation anomalies have little direct influence on California precipitation, especially during dry years, mainly because of their weak amplitudes. The California droughts cannot be readily attributed to the reduced ocean evaporation. The association between increased Pacific evaporation and floods over California is somewhat stronger.

Interactions between soil moisture, evapotranspiration (ET), atmospheric moisture fluxes and precipitation are complex. It is difficult to attribute the variations of one variable to another. In this study, we investigate the influence of atmospheric moisture fluxes and land surface soil moisture on local precipitation, with a focus on the southern United States (U.S.), a region with a strong humidity gradient and intense moisture fluxes. Experiments with the Weather Research and Forecasting model show that the variation of moisture flux convergence (MFC) is more important than that of soil moisture for precipitation variation over the southern U.S. Further analyses decompose the precipitation change into several contributing factors and show that MFC affects precipitation both directly through changing moisture inflow (wet areas) and indirectly by changing the precipitation efficiency (transitional zones). Soil moisture affects precipitation mainly by changing the precipitation efficiency, and secondly through direct surface ET contribution. The greatest soil moisture effects are over transitional zones. MFC is more important for the probability of heavier rainfall; soil moisture has much weaker impact on rainfall probability and its roles are similar for the probability of intermediate-to-heavy rainfall (>10 mm day−1). Although MFC is more important than soil moisture for precipitation over most regions, the impact of soil moisture could be large over certain transitional regions. At the submonthly time scale, the African Sahel appears to be the only major region where soil moisture has a greater impact than MFC on precipitation. This study provides guidance to understanding and further investigation of the roles of local land surface processes and large-scale circulations on precipitation.

The Southern Great Plains (SGP) has been shown as a region of significant soil moisture-precipitation (S-P) coupling. However, how strong evapotranspiration (ET) can affect regional precipitation remains largely unclear, impeding a full grasp of the S-P feedback in that area. The current study seeks to unravel, in a spring month (April), the potential role played by large-scale atmospheric conditions in shaping S (ET)-P feedback. Our regional climate modeling experiments demonstrate that the presence of anomalous low (high) pressure and cyclonic (anticyclonic) flows at the upper/middle troposphere over the relevant areas is associated with strongest (minimum) positive S-P feedback in the SGP. Their impacts are interpreted in terms of large-scale atmospheric dynamical disturbance, including the intensity and location of synoptic eddies. Further analyses of the vertical velocity fields corroborate these interpretations. In addition, the relationship between lower tropospheric moisture conditions (including winds) and feedback composites is evaluated.

A quasi-isentropic, back-trajectory scheme is applied to output from the Modern-Era Retrospective Analysis for Research and Applications (MERRA) and a land-only replay with corrected precipitation to estimate surface evaporative sources of moisture supplying precipitation over every ice-free land location for the period 1979–2005. The evaporative source patterns for any location and time period are effectively two-dimensional probability distributions. As such, the evaporative sources for extreme situations like droughts or wet intervals can be compared to the corresponding climatological distributions using the method of relative entropy. Significant differences are found to be common and widespread for droughts, but not wet periods, when monthly data are examined. At pentad temporal resolution, which is more able to isolate floods and situations of atmospheric rivers, values of relative entropy over North America are typically 50%–400% larger than at monthly time scales. Significant differences suggest that moisture transport may be a key factor in precipitation extremes. Where evaporative sources do not change significantly, it implies other local causes may underlie the extreme events.

Irrigation is an important human activity that may impact local and regional climate, but current climate model simulations and data assimilation systems generally do not explicitly include it. The European Centre for Medium-Range Weather Forecasts (ECMWF) Interim Re-Analysis (ERA-Interim) shows more irrigation signal in surface evapotranspiration (ET) than the Modern-Era Retrospective Analysis for Research and Applications (MERRA) because ERA-Interim adjusts soil moisture according to the observed surface temperature and humidity while MERRA has no explicit consideration of irrigation at the surface. But, when compared with the results from a hydrological model with detailed considerations of agriculture, the ET from both reanalyses show large deficiencies in capturing the impact of irrigation. Here, a back-trajectory method is used to estimate the contribution of irrigation to precipitation over local and surrounding regions, using MERRA with observation-based corrections and added irrigation-caused ET increase from the hydrological model. Results show substantial contributions of irrigation to precipitation over heavily irrigated regions in Asia, but the precipitation increase is much less than the ET increase over most areas, indicating that irrigation could lead to water deficits over these regions. For the same increase in ET, precipitation increases are larger over wetter areas where convection is more easily triggered, but the percentage increase in precipitation is similar for different areas. There are substantial regional differences in the patterns of irrigation impact, but, for all the studied regions, the highest percentage contribution to precipitation is over local land.

The operational coupled land–atmosphere forecast model from the National Centers for Environmental Prediction (NCEP) is evaluated for the strength and characteristics of its coupling in the water cycle between land and atmosphere. Following the protocols of the Global Land–Atmosphere Coupling Experiment (GLACE) it is found that the Global Forecast System (GFS) atmospheric model coupled to the Noah land surface model exhibits extraordinarily weak land–atmosphere coupling, much as its predecessor, the GFS–Oregon State University (OSU) coupled system. The coupling strength is evaluated by the ability of subsurface soil wetness to affect locally the time series of precipitation. The surface fluxes in Noah are also found to be rather insensitive to subsurface soil wetness. Comparison to another atmospheric model coupled to Noah as well as a different land surface model show that Noah is responsible for some of the lack of sensitivity, primarily because its thick (10 cm) surface layer dominates the variability in surface latent heat fluxes. Noah is found to be as responsive as other land surface models to surface soil wetness and temperature variations, suggesting the design of the GLACE sensitivity experiment (based only on subsurface soil wetness) handicapped the Noah model. Additional experiments, in which the parameterization of evapotranspiration is altered, as well as experiments where surface soil wetness is also constrained, isolate the GFS atmospheric model as the principal source of the weak sensitivity of precipitation to land surface states.

The Global Land–Atmosphere Coupling Experiment (GLACE) built a framework to estimate the strength of the land–atmosphere interaction across many weather and climate models. Within this framework, GLACE-type experiments are performed with a single atmospheric model coupled to three different land models. The precipitation time series is decomposed into three frequency bands to investigate the large-scale connection between external forcing, precipitation variability and predictability, and land–atmosphere coupling strength. It is found that coupling to different land models or prescribing subsurface soil moisture does not change the global pattern of precipitation predictability and variability too much. However, the regional impact of soil moisture can be highlighted by calculating the land–atmosphere coupling strength, which shows very different patterns for the three models. The estimated precipitation predictability and land–atmosphere coupling strength is mainly associated with the low-frequency component of precipitation (periods beyond 3 weeks). Based on these findings, the land–atmosphere coupling strength is conceptually decomposed into the impact of low-frequency external forcing and the impact of soil moisture. Because most models participating in GLACE have overestimated the low-frequency component of precipitation, a calibration to the GLACE-estimated land–atmosphere coupling strength is performed. The calibrated coupling strength is generally weaker, but the global pattern does not change much. This study provides an important clarification of land–atmosphere coupling strength and increases the understanding of the land–atmosphere interaction.

An atmospheric general circulation model (AGCM) is coupled to three different land surface schemes (LSSs), both individually and in combination (i.e., the LSSs receive the same AGCM forcing each time step and the averaged upward surface fluxes are passed back to the AGCM), to study the uncertainty of simulated climatologies and variabilities caused by different LSSs. This tiling of the LSSs is done to study the uncertainty of simulated mean climate and climate variability caused by variations between LSSs. The three LSSs produce significantly different surface fluxes over most of the land, no matter whether they are coupled individually or in combination. Although the three LSSs receive the same atmospheric forcing in the combined experiment, the inter-LSS spread of latent heat flux can be larger or smaller than the individually coupled experiment, depending mostly on the evaporation regime of the schemes in different regions. Differences in precipitation are the main reason for the different latent heat fluxes over semiarid regions, but for sensible heat flux, the atmospheric differences and LSS differences have comparable contributions. The influence of LSS uncertainties on the simulation of surface temperature is strongest in dry seasons, and its influence on daily maximum temperature is stronger than on minimum temperature. Land–atmosphere interaction can dampen the impact of LSS uncertainties on surface temperature in the tropics, but can strengthen their impact in middle to high latitudes. Variations in the persistence of surface heat fluxes exist among the LSSs, which, however, have little impact on the global pattern of precipitation persistence. The results provide guidance to future diagnosis of model uncertainties related to LSSs.

An atmospheric general circulation model (AGCM) and an oceanic general circulation model (OGCM) are asynchronously coupled to simulate the climate of the mid-Holocene period. The role of the solar radiation and ocean in the mid-Holocene East Asian monsoon climate is analyzed and some mechanisms are revealed. At the forcing of changed solar radiation induced by the changed orbital parameters and the changed SST simulated by the OGCM, compared with when there is orbital forcing alone, there is more precipitation and the monsoon is stronger in the summer of East Asia, and the winter temperature increases over China. These agree better with the reconstructed data. It is revealed that the change of solar radiation can displace northward the ITCZ and the East Asia subtropical jet, which bring more precipitation over the south of Tibet and North and Northeast China. By analyzing the summer meridional latent heat transport, it is found that the influence of solar radiation change is mainly to increase the convergence of atmosphere toward the land, and the influence of SST change is mainly to transport more moisture to the sea surface atmosphere. Their synergistic effect on East Asian precipitation is much stronger than the sum of their respective effects.

[1] The ability of soil moisture to affect precipitation (SM-P) can be dissected into the ability of soil moisture to affect evapotranspiration (ET; SM-ET) and the ability of ET to affect precipitation (ET-P). SM-ET is a local process that is relatively easy to quantify, but ET-P includes nonlocal atmospheric processes and is more complex. Here, ET-P is quantified both locally and remotely with a back-trajectory method for water vapor transport, using corrected reanalysis data. It is found that, for SM-P and ET-P, local impact is greater than that from remote for most land areas with significant local impacts. By examining the responses of the three metrics (SM-ET, ET-P, and SM-P) to climate variations over different climate regimes, we show that SM-ET is the principal factor that determines the spatial pattern and variation of SM-P. For climatologically wet regions, SM-ET and SM-P are higher during dry periods, and vice versa for climatologically dry regions. All three metrics show highest values over the transitional zones.

It is now well-established that the chaotic nature of the atmosphere severely limits the predictability of weather, while the slowly varying sea surface temperature (SST) and land surface states can enhance the predictability of atmospheric variations through surfaceatmosphere interactions or by providing a boundary condition (e.g., Shukla 1993, 1998; Shukla et al. 2000; Graham et al. 1994; Koster et al. 2000; Dirmeyer et al. 2003; Quan et al. 2004). Among them, the influence of ocean is more important on a global scale because it covers twice as much surface area as land and is a much larger heat and energy reservoir. But the impact of ocean may not be dominant over land, especially the mid-latitude land (Koster and Suarez 1995). The Global Land-Atmosphere Coupling Experiment (GLACE) (Koster et al., 2004, 2006) builds a framework to objectively estimate the potential contribution of land states to atmospheric predictability (called land-atmosphere coupling strength) in numerical weather and climate models. By averaging the estimated land-atmosphere coupling strength from 12 models participating in GLACE, an ensemble average coupling strength is obtained. However, the coupling strength varies widely among models. The discrepancy is certainly related to differences in the parameterization of processes and their complex interactions, from soil hydrology, vegetation physiology, to boundary layer, cloud and precipitation processes. It is difficult to determine what causes the relatively strong or weak coupling strengths seen in individual models. Some studies have identified the impact of soil moisture on evapotranspiration (ET) (denoted SM→ET) and the impact of ET on precipitation (denoted ET→P) as two key factors for land-atmosphere coupling (Guo et al. 2006 (hereafter GUO06); Dirmeyer et al. 2010). For soil moisture to have a strong impact on precipitation, both SM→ET and ET→P need to be strong. This usually happens in transitional zones between wet and dry climates (Dirmeyer 2006). In addition to the mean climate state, does the climate variability have some impact on land-atmosphere coupling? A theoretical study found that the strength of the external forcing can affect the coupling strength and the location of coupling hot spots (Wei et al. 2006). Even less is known about how the land-atmosphere coupling is related to the

[1] Two different Atmospheric General Circulation Models (AGCMs), each coupled to three different land surface schemes (LSSs) (six different model configurations in total), are used to study the roles of different model components and different action processes in land-atmosphere coupling. Experiments show that, for the six model configurations, the choice of AGCMs is the main reason for the substantially different precipitation variability, predictability, and land-atmosphere coupling strength among the configurations. The impact of different LSSs is secondary. Intraseasonal precipitation variability, which is mainly a property of the AGCM, can impact land-atmosphere coupling both directly in the atmosphere and indirectly through soil moisture response to precipitation. These results lead to a common conceptual decomposition of the land-atmosphere coupling strength and increases the understanding on large-scale land-atmosphere coupling.

[1] Precipitation analysis and soil moisture from the Global Land Data Assimilation System (GLDAS) are used to assess the land-atmosphere coupling in boreal summer. Correlations between antecedent soil moisture and precipitation suggest that regions of strong land-atmosphere coupling lie mainly in arid to semiarid transition zones or in semihumid forest to grassland transition zones. They consist of central Eurasia, the region from Mongolia to northern China, southwest China, the Sahel, the northern continental United States, and southern Europe. It is found that over these regions, positive soil moisture feedback accounts for typically 10–20% of the variance of monthly precipitation anomalies with the feedback efficiency of the order of 0.3–0.9 mm month−1 (0.1 standardized soil moisture)−1. While soil moisture feedback is dominated by the positive sign, negative feedback may exist in some areas, such as India and the western part and Quebec province of Canada. Generally, the land-atmosphere coupling strength estimated from the GLDAS data agrees well with those from the observational soil moisture in Illinois and the European Centre for Medium-Range Weather Forecasts 40-year reanalysis (ERA-40) soil moisture product. Physical mechanisms responsible for the findings are further discussed. This study provides a Northern Hemisphere distribution of the land-atmosphere coupling strength, which can be used to test the model simulations on monthly to seasonal time scales.

[1] By analyzing the data from the Second Global Soil Wetness Project (GSWP-2) sensitivity experiments, this study quantifies the sensitivities of soil wetness simulation to uncertainties in precipitation and radiation forcings and estimates the actual influence of these uncertainties. The sensitivity of soil moisture to uncertainties in radiation has a large seasonal cycle because of its temperature dependence. Sensitivity to uncertainties in precipitation is less temporally variable and is large in semiarid regions. Precipitation sensitivity is generally dominant over radiation sensitivity in cold climate, while in warm climate radiation sensitivity is dominant or both of them are high. However, actual uncertainties in precipitation are typically much larger than for net radiation in warm climate, so the practical impact is dominated by our lack of accurate precipitation estimates. Precipitation uncertainties account for about 2/3 of the total soil wetness uncertainties from all forcing data, while radiation uncertainties only account for about 1/6.

[1] A simple model is developed to describe the significant land-atmosphere interaction processes in the warm climate. It includes bulk soil hydrology, dynamic vegetation, and simple land-atmosphere interaction processes. The model can simulate the basic features of land surface control on evapotranspiration (ET) and exhibits a multiequilibrium behavior similar to that of some more complex models. In order to study the role of land surface processes in climate variability on monthly to seasonal timescales, a series of experiments are performed with the model over different land covers and at different external forcings. The major findings are: (1) The maximum soil wetness memory and precipitation predictability tend to occur at a sparser (denser) vegetation cover with the weakening (strengthening) of external forcing. (2) For vegetated region, the soil moisture memory and precipitation persistence will be underestimated if vegetation is not interactive, and the percentage of underestimation is larger over denser vegetation covers. (3) Interactive vegetation can enhance the low-frequency coherency between soil wetness and precipitation, but its influence on high-frequency coherency is small. (4) Large coherencies between soil wetness and precipitation in the time-frequency domain correspond to strong wavelet power of external forcing in the same domain. These findings provide guidance for the development of and study with more complex models.