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This article presents the "screen-level and surface analysis only" (SSA) system at the European Centre for Medium-Range Weather Forecasts (ECMWF). SSA is a simplification of the operational land–atmosphere weakly coupled data assimilation (WCDA). The goal of SSA is to provide 1) efficient research into land surface developments in NWP and 2) land reanalyses with land–atmosphere coupling. SSA maintains a coupled forecast model between assimilation cycles, but the atmospheric analysis is not performed; rather, it is forced from an archived analysis. Hence, SSA is much faster than WCDA, although it lacks feedback between the land and atmospheric analyses. A global sensitivity analysis was performed over one year to compare the WCDA and SSA systems. Prescribed proxy 2-m temperature/humidity screen-level observation errors were approximately doubled in the soil moisture data assimilation, thereby reducing the average size of the root-zone soil moisture analysis increments by about 60%. The systematic impact of these changes on the WCDA surface and near-surface atmospheric dynamics was effectively captured by SSA, although the short-term impact was underestimated. Importantly, the SSA forecast verification scores accurately reflected those of WCDA: atmospheric 1–10-day temperature/humidity forecasts were degraded in the tropics and lower midlatitudes up to about 700 hPa. The soil moisture analysis performance was not significantly impacted. These results endorse SSA as an NWP research tool and confirm the role of assimilating proxy screen-level observations in the soil moisture analysis to improve weather forecasts. Appropriate use and limitations of SSA are considered. [ABSTRACT FROM AUTHOR]

Anthropogenic activities have been significantly perturbing global freshwater flows and groundwater reserves. Despite numerous advances in the development of land surface models (LSMs) and global terrestrial hydrological models (GHMs), relatively few studies have attempted to simulate the impacts of anthropogenic activities on the terrestrial water cycle using the framework of LSMs. From the comparison of simulated terrestrial water storage with the Gravity Recovery and Climate Experiment (GRACE) satellite observations it is found that a process-based LSM, the Minimal Advanced Treatments of Surface Interaction and Runoff (MATSIRO), outperforms the bucket-model-based GHM called H08 in simulating hydrologic variables, particularly in water-limited regions. Therefore, the water regulation modules of H08 are incorporated into MATSIRO. Further, a new irrigation scheme based on the soil moisture deficit is developed. Incorporation of anthropogenic water regulation modules significantly improves river discharge simulation in the heavily regulated global river basins. Simulated irrigation water withdrawal for the year 2000 (2462 km3 yr−1) agrees well with the estimates provided by the Food and Agriculture Organization (FAO). Results indicate that irrigation changes surface energy balance, causing a maximum increase of ~50 W m−2 in latent heat flux averaged over June-August. Moreover, unsustainable anthropogenic water use in 2000 is estimated to be ~450 km3 yr−1, which corresponds well with documented records of groundwater overdraft, representing an encouraging improvement over the previous modeling studies. Globally, unsustainable water use accounts for ~40%% of blue water used for irrigation. The representation of anthropogenic activities in MATSIRO makes the model a suitable tool for assessing potential anthropogenic impacts on global water resources and hydrology. [ABSTRACT FROM AUTHOR]

In this paper, preliminary results are presented showing that the two record-setting extreme events during 2010 summer (i.e., the Russian heat wave-wildfires and Pakistan flood) were physically connected. It is found that the Russian heat wave was associated with the development of an extraordinarily strong and prolonged extratropical atmospheric blocking event in association with the excitation of a large-scale atmospheric Rossby wave train spanning western Russia, Kazakhstan, and the northwestern China-Tibetan Plateau region. The southward penetration of upper-level vorticity perturbations in the leading trough of the Rossby wave was instrumental in triggering anomalously heavy rain events over northern Pakistan and vicinity in mid- to late July. Also shown are evidences that the Russian heat wave was amplified by a positive feedback through changes in surface energy fluxes between the atmospheric blocking pattern and an underlying extensive land region with below-normal soil moisture. The Pakistan heavy rain events were amplified and sustained by strong anomalous southeasterly flow along the Himalayan foothills and abundant moisture transport from the Bay of Bengal in connection with the northward propagation of the monsoonal intraseasonal oscillation. [ABSTRACT FROM AUTHOR]

A revised framework for the analysis of soil moisture memory characteristics of climate models and observational data is derived from the approach proposed by Koster and Suarez. The resulting equation allows the expression of the month-to-month soil moisture autocorrelation as a function of 1) the initial soil moisture variability, 2) the (atmospheric) forcing variability over the considered time period, 3) the correlation between initial soil moisture and subsequent forcing, 4) the sensitivity of evaporation to soil moisture, and 5) the sensitivity of runoff to soil moisture. A specific new feature is the disentangling of the roles of initial soil moisture variability and forcing variability, which were both (for the latter indirectly) contributing to the seasonality term of the original formulation. In addition, a version of the framework entirely based on explicit equations for the underlying relationships (i.e., independent of soil moisture statistics at the following time step) is proposed. The validity of the derived equation is exemplified with atmospheric general circulation model (AGCM) simulations from the Global Land-Atmosphere Coupling Experiment (GLACE). [ABSTRACT FROM AUTHOR]

A synthesis of several approaches to quantifying land–atmosphere interactions is presented. These approaches use data from observations or atmospheric reanalyses applied to atmospheric tracer models and stand-alone land surface schemes. None of these approaches relies on the results of general circulation model simulations. A high degree of correlation is found among these independent approaches, and constructed here is a composite assessment of global land–atmosphere feedback strength as a function of season. The composite combines the characteristics of persistence of soil moisture anomalies, strong soil moisture regulation of evaporation rates, and reinforcement of water cycle anomalies through recycling. The regions and seasons that have a strong composite signal predominate in both summer and winter monsoon regions in the period after the rainy season wanes. However, there are exceptions to this pattern, most notably over the Great Plains of North America and the Pampas/Pantanal of South America, where there are signs of land–atmosphere feedback throughout most of the year. Soil moisture memory in many of these regions is long enough to suggest that real-time monitoring and accurate initialization of the land surface in forecast models could lead to improvements in medium-range weather to subseasonal climate forecasts. [ABSTRACT FROM AUTHOR]

A key issue in modeling the Sahelian climate is to correctly predict the energy fluxes between the land surface and the atmosphere. A problem faced by land surface models in the Sahel is the horizontal heterogeneity of soil and vegetation properties in the region, where measured data are scarce. Experiments have been designed to evaluate a land surface model both in offline mode and coupled to the Advanced Regional Prediction System (ARPS), a mesoscale atmospheric model. For the evaluation in offline mode, an observational dataset of 58 days from the Hydrological and Atmospheric Pilot Experiment in the Sahel (HAPEX-Sahel) is gathered to interpret the results. For the evaluation in the coupled mode, boundary layer development is simulated for 4 individual days. The model is able to reproduce the observations close to measurement errors. Sensitivity experiments are conducted to identify the most important parameters that affect the simulation of the convective available potential energy (CAPE) and the equivalent potential temperature (θe), two variables closely linked to convection and rainfall in the Sahel. During the selected cases, model results depend significantly on the soil moisture conditions. When bare soil evaporation is dominant, the soil parameters have the most impact on CAPE and θe, whereas in dry soil conditions, the vegetation parameters gain in importance. Differences due to the change of one parameter can reach 400 J kg-1 for CAPE and 4 K for θe, which is large enough to affect local convection. [ABSTRACT FROM AUTHOR]

Because precipitation and net radiation in an atmospheric general circulation model (AGCM) are typically biased relative to observations, the simulated evaporative regime of a region may be biased, with consequent negative effects on the AGCM’s ability to translate an initialized soil moisture anomaly into an improved seasonal prediction. These potential problems are investigated through extensive offline analyses with the Mosaic land surface model (LSM). The LSM was first forced globally with a 15-yr observation-based dataset. The simulation was then repeated after imposing a representative set of GCM climate biases onto the forcings—the observational forcings were scaled so that their mean seasonal cycles matched those simulated by the NASA Seasonal-to-Interannual Prediction Project (NSIPP-1; NASA Global Modeling and Assimilation Office) AGCM over the same period. The AGCM’s climate biases do indeed lead to significant biases in evaporative regime in certain regions, with the expected impacts on soil moisture memory time scales. Furthermore, the offline simulations suggest that the biased forcing in the AGCM should contribute to overestimated feedback in certain parts of North America—parts already identified in previous studies as having excessive feedback. The present study thus supports the notion that the reduction of climate biases in the AGCM will lead to more appropriate translations of soil moisture initialization into seasonal prediction skill. [ABSTRACT FROM AUTHOR]

A simple model is developed to investigate the role of spatial dynamics in the soil-atmosphere system. The model is constructed by considering the mass and energy balance equations for soil and atmosphere, closed with a two-dimensional, corrected quasigeostrophic approximation for large-scale atmospheric motions, and a suitable model for rainfall occurrence. The main result presented concerns the linear stability analyses of the homogenous equilibrium solutions for dry and wet climates. In the first case (desert) the system is stable and no spatial perturbation internal to the system can produce spatial heterogeneities. In the second case (wet climate) the dynamics can produce spatial instabilities of several scales, resulting in likely patterns of wet and dry zones. A key role in triggering this instability is played by the sensible heat flux to the atmosphere, which in turn is linked to soil moisture. [ABSTRACT FROM AUTHOR]