scholarly journals The Impact of Land Surface and Atmospheric Initialization on Seasonal Forecasts with CCSM

2012 ◽  
Vol 25 (3) ◽  
pp. 1007-1021 ◽  
Author(s):  
Daniel A. Paolino ◽  
James L. Kinter ◽  
Ben P. Kirtman ◽  
Dughong Min ◽  
David M. Straus
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Large Scale ◽  
Climate Model ◽  
Seasonal Forecast ◽  
Good Representation ◽  
Seasonal Forecasts ◽  
Coupled Models ◽  
Climate System Model ◽  
The Impact

Abstract Series of forecast experiments for two seasons investigate the impact of specifying realistic initial states of the land in conjunction with the observed states of the ocean and atmosphere while using the National Center for Atmospheric Research (NCAR) Community Climate System Model, version 3 (CCSM3.0). Since direct soil moisture observations adequate for initialization of the land surface do not exist, this study considers proxy data. The authors are able to successfully initialize all components of the CCSM3.0 and produce a good representation of the mean land surface climate in the first season’s forecast. In comparison with a previous set of forecast experiments that had initialized only the observed ocean state, there is firm evidence that this study produces a better representation of the interannual variability of the soil surface. The representation of soil moisture in the fully initialized seasonal forecasts as measured against the reanalysis is improved, due in part to the ability of the CCSM3.0 to persist large-scale anomalies present in the initial soil state. The improvement in the representation of the land surface, in conjunction with the atmospheric initialization, contributes to a skillful seasonal forecast of surface temperature. There is little evidence of an improved forecast of precipitation over land. Results from this study support the use of the CCSM, originally designed for use as a climate model, as a fully initialized seasonal forecast model. The authors suggest that initialization of the land surface state is crucial for skillful seasonal forecasts made with fully coupled models.

Land–Atmosphere Interaction in Tropical Africa

2020 ◽  
Author(s):  
Cathy Hohenegger
Keyword(s):  
Heat Flux ◽  
Soil Moisture ◽  
Latent Heat ◽  
Latent Heat Flux ◽  
Surface State ◽  
Land Surface ◽  
Large Scale ◽  
Moisture Distribution ◽  
Sahel Region ◽  
The Impact

Even though many features of the vegetation and of the soil moisture distribution over Africa reflect its climatic zones, the land surface has the potential to feed back on the atmosphere and on the climate of Africa. The land surface and the atmosphere communicate via the surface energy budget. A particularly important control of the land surface, besides its control on albedo, is on the partitioning between sensible and latent heat flux. In a soil moisture-limited regime, for instance, an increase in soil moisture leads to an increase in latent heat flux at the expanse of the sensible heat flux. The result is a cooling and a moistening of the planetary boundary layer. On the one hand, this thermodynamically affects the atmosphere by altering the stability and the moisture content of the vertical column. Depending on the initial atmospheric profile, convection may be enhanced or suppressed. On the other hand, a confined perturbation of the surface state also has a dynamical imprint on the atmospheric flow by generating horizontal gradients in temperature and pressure. Such gradients spin up shallow circulations that affect the development of convection. Whereas the importance of such circulations for the triggering of convection over the Sahel region is well accepted and well understood, the effect of such circulations on precipitation amounts as well as on mature convective systems remains unclear. Likewise, the magnitude of the impact of large-scale perturbations of the land surface state on the large-scale circulation of the atmosphere, such as the West African monsoon, has long been debated. One key issue is that such interactions have been mainly investigated in general circulation models where the key involved processes have to rely on uncertain parameterizations, making a definite assessment difficult.

scholarly journals Impact of Scale and Aggregation on the Terrestrial Water Exchange: Integrating Land Surface Models and Rhône Catchment Observations

2007 ◽  
Vol 8 (5) ◽  
pp. 1002-1015 ◽  
Author(s):  
Reto Stöckli ◽  
Pier Luigi Vidale ◽  
Aaron Boone ◽  
Christoph Schär
Keyword(s):  
Soil Moisture ◽  
Spatial Resolution ◽  
Land Surface ◽  
Climate Model ◽  
Good Representation ◽  
Subgrid Scale ◽  
Land Surface Models ◽  
Catchment Scale ◽  
Surface Models ◽  
Terrestrial Water

Abstract Land surface models (LSMs) used in climate modeling include detailed above-ground biophysics but usually lack a good representation of runoff. Both processes are closely linked through soil moisture. Soil moisture however has a high spatial variability that is unresolved at climate model grid scales. Physically based vertical and horizontal aggregation methods exist to account for this scaling problem. Effects of scaling and aggregation have been evaluated in this study by performing catchment-scale LSM simulations for the Rhône catchment. It is found that evapotranspiration is not sensitive to soil moisture over the Rhône but it largely controls total runoff as a residual of the terrestrial water balance. Runoff magnitude is better simulated when the vertical soil moisture fluxes are resolved at a finer vertical resolution. The use of subgrid-scale topography significantly improves both the timing of runoff on the daily time scale (response to rainfall events) and the magnitude of summer baseflow (from seasonal groundwater recharge). Explicitly accounting for soil moisture as a subgrid-scale process in LSMs allows one to better resolve the seasonal course of the terrestrial water storage and makes runoff insensitive to the used grid scale. However, scale dependency of runoff to above-ground hydrology cannot be ignored: snowmelt runoff from the Alpine part of the Rhône is sensitive to the spatial resolution of the snow scheme, and autumnal runoff from the Mediterranean part of the Rhône is sensitive to the spatial resolution of precipitation.

scholarly journals Role of Soil Moisture Feedback in the Development of Extreme Summer Drought and Flood in the United States

2016 ◽  
Vol 17 (8) ◽  
pp. 2191-2207 ◽  
Author(s):  
Roop Saini ◽  
Guiling Wang ◽  
Jeremy S. Pal
Keyword(s):  
United States ◽  
Soil Moisture ◽  
Large Scale ◽  
Climate Model ◽  
The United States ◽  
Summer Drought ◽  
Large Set ◽  
Drought And Flood ◽  
The Impact ◽  
Soil Moisture Feedback

Abstract This study tackles the contribution of soil moisture feedback to the development of extreme summer precipitation anomalies over the conterminous United States using a regional climate model. The model performs well in reproducing both the mean climate and extremes associated with drought and flood. A large set of experiments using the model are conducted that involve swapped initial soil moisture between flood and drought years using the 1988 and 2012 droughts and 1993 flood as examples. The starting time of these experiments includes 1 May (late spring) and 1 June (early summer). For all three years, the impact of 1 May soil moisture swapping is much weaker than the 1 June soil moisture swapping. In 1988 and 2012, replacing the 1 June soil moisture with that from 1993 reduces both the spatial extent and the severity of the simulated summer drought and heat. The impact is especially strong in 2012. In 1993, however, replacing the 1 June soil moisture with that from 1988 has little impact on precipitation. The contribution of soil moisture feedback to summer extremes is larger in 2012 than in 1988 and 1993. This may be because of the presence of strong anomalies in large-scale forcing in 1988 and 1993 that prohibit or favor precipitation, and the lack of such in 2012. This study demonstrates how the contribution of land–atmosphere feedback to the development of seasonal climate anomalies may vary from year to year and highlights its importance in the 2012 drought.

scholarly journals Impact of Atmosphere and Land Surface Initial Conditions on Seasonal Forecasts of Global Surface Temperature

2014 ◽  
Vol 27 (24) ◽  
pp. 9253-9271 ◽  
Author(s):  
Stefano Materia ◽  
Andrea Borrelli ◽  
Alessio Bellucci ◽  
Andrea Alessandri ◽  
Pierluigi Di Pietro ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Initial Conditions ◽  
Seasonal Prediction ◽  
Forecast Skill ◽  
Seasonal Forecasts ◽  
Initial State ◽  
Predictive Skill ◽  
The Impact ◽  
Initialization Strategy

Abstract The impact of land surface and atmosphere initialization on the forecast skill of a seasonal prediction system is investigated, and an effort to disentangle the role played by the individual components to the global predictability is done, via a hierarchy of seasonal forecast experiments performed under different initialization strategies. A realistic atmospheric initial state allows an improved equilibrium between the ocean and overlying atmosphere, increasing the model predictive skill in the ocean. In fact, in regions characterized by strong air–sea coupling, the atmosphere initial condition affects forecast skill for several months. In particular, the ENSO region, eastern tropical Atlantic, and North Pacific benefit significantly from the atmosphere initialization. On the mainland, the effect of atmospheric initial conditions is detected in the early phase of the forecast, while the quality of land surface initialization affects forecast skill in the following seasons. Winter forecasts in the high-latitude plains benefit from the snow initialization, while the impact of soil moisture initial state is particularly effective in the Mediterranean region and central Asia. However, the initialization strategy based on the full value technique may not be the best choice for land surface, since soil moisture is a strongly model-dependent variable: in fact, initialization through land surface reanalysis does not systematically guarantee a skill improvement. Nonetheless, using a different initialization strategy for land, as opposed to atmosphere and ocean, may generate inconsistencies. Overall, the introduction of a realistic initialization for land and atmosphere substantially increases skill and accuracy. However, further developments in the procedure for land surface initialization are required for more accurate seasonal forecasts.

scholarly journals Optimizing a backscatter forward operator using Sentinel-1 data over irrigated land

2021 ◽  
Vol 25 (12) ◽  
pp. 6283-6307
Author(s):  
Sara Modanesi ◽  
Christian Massari ◽  
Alexander Gruber ◽  
Hans Lievens ◽  
Angelica Tarpanelli ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Land Surface ◽  
Large Scale ◽  
Bayesian Optimization ◽  
Irrigation Scheme ◽  
Area Index ◽  
Observation Operator ◽  
Future Data ◽  
The Impact

Abstract. Worldwide, the amount of water used for agricultural purposes is rising, and the quantification of irrigation is becoming a crucial topic. Because of the limited availability of in situ observations, an increasing number of studies is focusing on the synergistic use of models and satellite data to detect and quantify irrigation. The parameterization of irrigation in large-scale land surface models (LSMs) is improving, but it is still hampered by the lack of information about dynamic crop rotations, or the extent of irrigated areas, and the mostly unknown timing and amount of irrigation. On the other hand, remote sensing observations offer an opportunity to fill this gap as they are directly affected by, and hence potentially able to detect, irrigation. Therefore, combining LSMs and satellite information through data assimilation can offer the optimal way to quantify the water used for irrigation. This work represents the first and necessary step towards building a reliable LSM data assimilation system which, in future analysis, will investigate the potential of high-resolution radar backscatter observations from Sentinel-1 to improve irrigation quantification. Specifically, the aim of this study is to couple the Noah-MP LSM running within the NASA Land Information System (LIS), with a backscatter observation operator for simulating unbiased backscatter predictions over irrigated lands. In this context, we first tested how well modelled surface soil moisture (SSM) and vegetation estimates, with or without irrigation simulation, are able to capture the signal of aggregated 1 km Sentinel-1 backscatter observations over the Po Valley, an important agricultural area in northern Italy. Next, Sentinel-1 backscatter observations, together with simulated SSM and leaf area index (LAI), were used to optimize a Water Cloud Model (WCM), which will represent the observation operator in future data assimilation experiments. The WCM was calibrated with and without an irrigation scheme in Noah-MP and considering two different cost functions. Results demonstrate that using an irrigation scheme provides a better calibration of the WCM, even if the simulated irrigation estimates are inaccurate. The Bayesian optimization is shown to result in the best unbiased calibrated system, with minimal chances of having error cross-correlations between the model and observations. Our time series analysis further confirms that Sentinel-1 is able to track the impact of human activities on the water cycle, highlighting its potential to improve irrigation, soil moisture, and vegetation estimates via future data assimilation.

scholarly journals Impact of Snow Representation in Seasonal Forecast Systems

2021 ◽  
Author(s):  
Danny Risto ◽  
Kristina Fröhlich ◽  
Bodo Ahrens
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Snow Water Equivalent ◽  
Winter Season ◽  
Seasonal Forecast ◽  
Radiation Budget ◽  
Climate Services ◽  
Temperature And Precipitation ◽  
Time Lagged ◽  
The Impact

<p>Current seasonal forecast systems have difficulties predicting temperature over continental regions, whereas for some regions with maritime influence their performance is better. The main driver for better skill in maritime regions is related to the ocean and its memory effect. For continental regions, the land surface can become a more important source of predictability on (sub-)seasonal time scales. Besides soil moisture, snow is a crucial component of the land surface as it stores an extensive amount of water and modulates the earth’s radiation budget each winter season. A snow-covered land surface leads to local temperature decreases in the overlying air (snow-albedo effect and high emissivity) and melting snow cools the surface air and contributes to soil moisture and river water. We compare the snow representation in seasonal forecast systems from four European weather/climate services provided by the Copernicus Climate Change Service (C3S) and their performance in predicting snow, temperature and precipitation. The goal is to identify the impact of the snow initialisation and snow modelling from the four forecasts systems. The first results show that the predicted anomalies of 2m temperature over continental regions correlate with reanalyses only for the first forecasted month, whereas anomalies in snow water equivalent can be predicted up to several months. While the biases among the forecast systems differ, the correlation skills are similar for the same variable, with precipitation having the lowest correlation skills. Furthermore, we will investigate the causal relationships between snow and 2m temperature with time-lagged correlation or similar methods and will consider the whole ensembles of the hindcasts.</p>

scholarly journals Land surface controls on afternoon precipitation diagnosed from observational data: uncertainties, confounding factors and the possible role of vegetation interception

2013 ◽  
Vol 13 (11) ◽  
pp. 29137-29201 ◽  
Author(s):  
B. P. Guillod ◽  
B. Orlowsky ◽  
D. Miralles ◽  
A. J. Teuling ◽  
P. Blanken ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Forest Cover ◽  
Remote Sensing Data ◽  
Strong Relationship ◽  
Heat Fluxes ◽  
Turbulent Heat ◽  
Seasonal Forecasts ◽  
The Impact

Abstract. The feedback between soil moisture and precipitation has long been a topic of interest due to its potential for improving weather and seasonal forecasts. The generally proposed mechanism assumes a control of soil moisture on precipitation via the partitioning of the surface turbulent heat fluxes, as assessed via the Evaporative Fraction, EF, i.e. the ratio of latent heat to the sum of latent and sensible heat, in particular under convective conditions. Our study investigates the poorly understood link between EF and precipitation by investigating the impact of before-noon EF on the frequency of afternoon precipitation over the contiguous US, using a statistical analysis of the relationship between multiple datasets of EF and precipitation. We analyze remote sensing data products (EF from GLEAM, Global Land Evaporation: the Amsterdam Methodology, based on satellite observations; and radar precipitation from NEXRAD, the NEXt generation weather RADar system), FLUXNET station data, and the North American Regional Reanalysis (NARR). While most datasets agree on the existence of regions of positive relationship between between EF and precipitation in the Eastern and Southwestern US, observation-based estimates (GLEAM, NEXRAD and to some extent FLUXNET) also indicate a strong relationship in the Central US which is not found in NARR. Investigating these differences, we find that much of these relationships can be explained by precipitation persistence alone, with ambiguous results on the additional role of EF in causing afternoon precipitation. Regional analyses reveal contrasting mechanisms over different regions. Over the Eastern US, our analyses suggest that the apparent EF-precipitation coupling takes place on a short day-to-day time scale and is either atmospherically controlled (from precipitation persistence and potential evaporation) or driven by vegetation interception and subsequent re-evaporation (rather than soil moisture and related plant transpiration/bare soil evaporation), in line with the high forest cover and the wet regime of that region. Over the Central and Southwestern US, the impact of EF on convection triggering is additionally linked to soil moisture variations, owing to the soil moisture–limited climate regime.

scholarly journals The Continental-Scale Soil-Moisture Precipitation Feedback in Europe with Parameterized and Explicit Convection

2021 ◽  
pp. 1-56
Author(s):  
David Leutwyler ◽  
Adel Imamovic ◽  
Christoph Schär
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Climate Model ◽  
Regional Climate ◽  
Summer Precipitation ◽  
Positive Sign ◽  
Precipitation Frequency ◽  
Continental Scale ◽  
The Impact ◽  
Precipitation Events

AbstractSoil moisture atmosphere interactions are key elements of the regional climate system. There is a well-founded hope that a more accurate representation of the soil moisture-precipitation feedback would improve the simulation of summer precipitation on daily to seasonal, to climate time scales. However, uncertainties have persistently remained as the simulated feedback is strongly sensitive to the model representation of deep convection. Here we assess the feedback representation using a GPU-accelerated version of the regional climate model COSMO. We simulate and compare the impact of continental-scale springtime soil-moisture anomalies on summer precipitation at convection-resolving (2.2 km) and convection-parameterizing resolution (12 km). We conduct re-analysis-driven simulations of 10 summer seasons (1999-2008) in continental Europe. While both simulations qualitatively agree on a positive sign of soil moisture-induced precipitation, they strongly differ in precipitation frequency: When convection is parameterized, wetter soil predominantly leads to more frequent precipitation events, and when convection is treated explicitly, they primarily become more intense. The results indicate that the sensitivity to soil moisture is stronger with parameterized convection, suggesting that the land surface-atmosphere coupling may be overestimated. In addition, the feedback’s sensitivity in complex terrain is assessed for soil perturbations of different horizontal scales. The convection-resolving simulations confirm a negative feedback for sub-continental soil moisture anomalies, which manifests itself in a local decrease of wet-hour frequency. However, the intensity feedback reinforces precipitation events at the same time (positive feedback). The two processes are represented differently in simulations with explicit and parameterized convection, explaining much of the difference between the two simulations.

scholarly journals Land surface modeling over the Dry Chaco: the impact of model structures, and soil, vegetation and land cover parameters

2021 ◽  
Vol 25 (7) ◽  
pp. 4099-4125 ◽  
Author(s):  
Michiel Maertens ◽  
Gabriëlle J. M. De Lannoy ◽  
Sebastian Apers ◽  
Sujay V. Kumar ◽  
Sarith P. P. Mahanama
Keyword(s):  
Soil Moisture ◽  
Land Cover ◽  
Land Surface ◽  
Water Budget ◽  
Large Scale ◽  
Vegetation Indices ◽  
Soil Parameters ◽  
Area Index ◽  
Vegetation Fraction ◽  
The Impact

Abstract. In this study, we tested the impact of a revised set of soil, vegetation and land cover parameters on the performance of three different state-of-the-art land surface models (LSMs) within the NASA Land Information System (LIS). The impact of this revision was tested over the South American Dry Chaco, an ecoregion characterized by deforestation and forest degradation since the 1980s. Most large-scale LSMs may lack the ability to correctly represent the ongoing deforestation processes in this region, because most LSMs use climatological vegetation indices and static land cover information. The default LIS parameters were revised with (i) improved soil parameters, (ii) satellite-based interannually varying vegetation indices (leaf area index and green vegetation fraction) instead of climatological vegetation indices, and (iii) yearly land cover information instead of static land cover. A relative comparison in terms of water budget components and “efficiency space” for various baseline and revised experiments showed that large regional and long-term differences in the simulated water budget partitioning relate to different LSM structures, whereas smaller local differences resulted from updated soil, vegetation and land cover parameters. Furthermore, the different LSM structures redistributed water differently in response to these parameter updates. A time-series comparison of the simulations to independent satellite-based estimates of evapotranspiration and brightness temperature (Tb) showed that no LSM setup significantly outperformed another for the entire region and that not all LSM simulations improved with updated parameter values. However, the revised soil parameters generally reduced the bias between simulated surface soil moisture and pixel-scale in situ observations and the bias between simulated Tb and regional Soil Moisture Ocean Salinity (SMOS) observations. Our results suggest that the different hydrological responses of various LSMs to vegetation changes may need further attention to gain benefits from vegetation data assimilation.

scholarly journals Impact of Soil Moisture Anomalies on Summer Rainfall over East Asia: A Regional Climate Model Study

2007 ◽  
Vol 20 (23) ◽  
pp. 5732-5743 ◽  
Author(s):  
Jung-Eun Kim ◽  
Song-You Hong
Keyword(s):  
Soil Moisture ◽  
East Asia ◽  
Large Scale ◽  
Climate Model ◽  
Regional Climate ◽  
Summer Rainfall ◽  
Department Of Energy ◽  
Initial Soil Moisture ◽  
Initial Soil ◽  
The Impact

Abstract Numerous modeling studies have shown that soil moisture anomalies in later spring have a significant effect on the summer rainfall anomalies in North America. On the other hand, the role of soil moisture in forming monsoonal precipitation in East Asia has not been identified. This study attempts to clarify the importance of soil moisture on the summer rainfall in late spring in East Asia. The National Centers for Environmental Prediction (NCEP) Regional Spectral Model (RSM) is utilized for 3-month (June–August) simulations in 1998 (above-normal precipitation year) and 1997 (below-normal precipitation year). Initial and boundary conditions are derived from the NCEP–Department of Energy (DOE) reanalysis. The control run uses the initial soil moisture from the reanalysis, whereas it is set as a saturation and wilting point for “wet” and “dry” experiments, respectively. The impact of soil moisture anomalies on the simulated summer rainfall in East Asia is not significant. The change in precipitation between the wet and dry experiments is about 10%. A conflict between the local feedback of soil moisture and a change in large-scale circulations associated with the summertime monsoonal circulation in East Asia can be attributed as a reason for this anomaly. It is found that enhanced (suppressed) evaporation from the soil to the atmosphere in wet (dry) initial soil moisture reduces (increases) the land–sea contrast between East Asia and the Pacific Ocean, leading to a weakened sensitivity of the monsoonal circulations to the initial soil moisture. It can be concluded that the impact of the initial soil moisture is significant on the dynamic circulation in East Asia.

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