scholarly journals Inter-comparison of two land-surface schemes applied on different scales and their feedbacks while coupled with a regional climate model

2011 ◽  
Vol 8 (4) ◽  
pp. 7091-7136 ◽  
Author(s):  
F. Zabel ◽  
W. Mauser ◽  
T. Marke ◽  
A. Pfeiffer ◽  
G. Zängl ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Water Stress ◽  
Regional Climate Model ◽  
Land Surface ◽  
Climate Model ◽  
Regional Climate ◽  
Land Surface Models ◽  
Feedback Effects ◽  
Surface Models ◽  
Vegetation Water

Abstract. Feedback effects between the land surface and the atmosphere are an important issue in modelling the climate system. Therefore, in order to take land surface heterogeneity adequately into account, a representation of the land surface in sufficient spatial resolution is necessary. In order to analyze the impact of different land surface models on the atmosphere, we analyzed the differences of two physically based land surface models, which evolved from different disciplinary backgrounds, both fully coupled with the regional climate model MM5, providing the atmospheric drivers. While the NOAH-LSM originally was developed for atmosphere applications, PROMET is primarily used as a hydrological land surface model. Both use different physical approaches and different spatial resolutions of 45 km (NOAH) and 1 km (PROMET) respectively, to represent the land surface processes. The parameterization of soil and plant properties in terms of phenological behaviour and water-stress is treated with a higher level of detail in PROMET. Used with same atmospheric drivers over a four-year period for Central Europe, the model differences have strong impacts on simulated evapotranspiration and soil moisture both spatially and temporally. Regions with high proportion of impervious surfaces show the highest differences in simulated evapotranspiration (up to 30 %). Further, PROMET simulations show lower evapotranspiration rates e.g. in the Po Valley, caused mainly by a higher level of vegetation water stress. In order to study feedback effects, PROMET was then bilaterally coupled with MM5. The feedbacks result in increasing near surface air temperature and decreasing precipitation especially in Southern Europe and are a result of regional self-amplification effects due to decreasing soil moisture and increasing vegetation water stress.

scholarly journals Results from a full coupling of the HIRHAM regional climate model and the MIKE SHE hydrological model for a Danish catchment

2014 ◽  
Vol 11 (3) ◽  
pp. 3005-3047 ◽  
Author(s):  
M. A. D. Larsen ◽  
J. C. Refsgaard ◽  
M. Drews ◽  
M. B. Butts ◽  
K. H. Jensen ◽  
...  
Keyword(s):  
Regional Climate Model ◽  
Land Surface ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Research Field ◽  
Land Surface Models ◽  
Mike She ◽  
Simulated Precipitation ◽  
Surface Models

Abstract. In recent years research on the coupling of existing regional climate models and hydrology/land surface models has emerged. A major challenge in this emerging research field is the computational interaction between the models. In this study we present results from a full two-way coupling of the HIRHAM regional climate model over a 4000 km x 2800 km domain in 11 km resolution and the combined MIKE SHE-SWET hydrology and land surface models over the 2500 km2 Skjern river catchment. A total of 26 one-year runs were performed to assess the influence of the data transfer interval (DTI) between the two models and the internal HIRHAM model variability of ten variables. In general, the coupled model simulations exhibit less accurate performance than the uncoupled simulations which is to be expected as both models prior to this study have been individually refined or calibrated to reproduce observations. Four of six output variables from HIRHAM, precipitation, relative humidity, wind speed and air temperature, showed statistically significant improvements in RMSE with a reduced DTI as evaluated in the range of 12–120 min. For these four variables the perturbation induced HIRHAM variability was shown to correspond to 47% of the RMSE improvement when using a DTI of 120 min compared to a DTI of 12 min and the variability resulted in large ranges in simulated precipitation. Also, the DTI was shown to substantially affect computation time. The MIKE SHE energy flux and discharge output variables experienced little impact from the DTI.

scholarly journals A multivariate comparison of two land-surface models integrated into an Arctic Regional Climate System model

1997 ◽  
Vol 25 ◽  
pp. 127-131
Author(s):  
Amanda Lynch ◽  
David McGinnis ◽  
William L. Chapman ◽  
Jeffrey S. Tilley
Keyword(s):  
Regional Climate Model ◽  
Land Surface ◽  
Climate Model ◽  
Regional Climate ◽  
Climate System ◽  
System Model ◽  
Small Scale ◽  
Land Surface Models ◽  
Climate System Model ◽  
Surface Models

Different vegetation models impact the atmospheric response of a regional climate model in different ways, and hence have an impact upon the ability of that model to match an observed climatology. Using a multivariate principal-component analysis, we investigate the relationships between several land-surface models (BATS, LSM) coupled to a regional climate model, and observed climate parameters over the North Slope of Alaska. In this application, annual cycle simulations at 20 km spatial resolution are compared with European Centre for Medium-Range Weather Forecasts (ECMWF) climatology. Initial results demonstrate broad agreement between all models; however, small-scale regional variations between land-surface models indicate the strengths and weaknesses of the land-surface treatments in a climate system model. Specifically, we found that the greater surface-moisture availability and temperature-dependent albedo formulation of the LSM model allow for a higher proportion of low-level cloud, and a later, more rapid transition from the winter to the summer regime. Crucial to this transition is the seasonal cycle of incoming solar radiation. These preliminary results indicate the importance of the land-surface hydrologic cycle in modelling the seasonal transitions.

scholarly journals A multivariate comparison of two land-surface models integrated into an Arctic Regional Climate System model

1997 ◽  
Vol 25 ◽  
pp. 127-131
Author(s):  
Amanda Lynch ◽  
David McGinnis ◽  
William L. Chapman ◽  
Jeffrey S. Tilley
Keyword(s):  
Regional Climate Model ◽  
Land Surface ◽  
Climate Model ◽  
Regional Climate ◽  
Climate System ◽  
System Model ◽  
Small Scale ◽  
Land Surface Models ◽  
Climate System Model ◽  
Surface Models

Different vegetation models impact the atmospheric response of a regional climate model in different ways, and hence have an impact upon the ability of that model to match an observed climatology. Using a multivariate principal-component analysis, we investigate the relationships between several land-surface models (BATS, LSM) coupled to a regional climate model, and observed climate parameters over the North Slope of Alaska. In this application, annual cycle simulations at 20 km spatial resolution are compared with European Centre for Medium-Range Weather Forecasts (ECMWF) climatology. Initial results demonstrate broad agreement between all models; however, small-scale regional variations between land-surface models indicate the strengths and weaknesses of the land-surface treatments in a climate system model. Specifically, we found that the greater surface-moisture availability and temperature-dependent albedo formulation of the LSM model allow for a higher proportion of low-level cloud, and a later, more rapid transition from the winter to the summer regime. Crucial to this transition is the seasonal cycle of incoming solar radiation. These preliminary results indicate the importance of the land-surface hydrologic cycle in modelling the seasonal transitions.

Sensitivities of isoprene emissions to soil moisture and impacts on surface ozone levels as simulated over the Euro-Mediterranean region by the regional climate model RegCM4.7chem-CLM4.5-MEGAN2.1

2021 ◽  
Author(s):  
Susanna Strada ◽  
Andrea Pozzer ◽  
Graziano Giuliani ◽  
Erika Coppola ◽  
Fabien Solmon ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Water Stress ◽  
Regional Climate Model ◽  
Mediterranean Region ◽  
Control Experiment ◽  
Climate Model ◽  
Surface Ozone ◽  
Regional Climate ◽  
Activity Factor ◽  
Ozone Levels

<p>In response to changes in environmental conditions (e.g., temperature, radiation, soil moisture), plants emit biogenic volatile organic compounds (BVOCs). In the large family of BVOCs, isoprene dominates and plays an important role in atmospheric chemistry. Once released in the atmosphere, isoprene influences levels of ozone, thus affecting both climate and air quality. In turn, climate change may alter isoprene emissions by increasing the occurrence and intensity of severe thermal and water stresses that alter plant functioning. To better constrain the evolution of isoprene emissions under future climates, it is critical to reduce the uncertainties in global and regional estimates of isoprene under present climate. Part of these uncertainties is related to the impact of water stress on isoprene. Recently, the BVOC emission model MEGAN has adopted a more sophisticated soil moisture activity factor γ<sub>sm</sub> which does not only account, as previously, for soil moisture available to plants but also links isoprene emissions to photosynthesis and plant water stress.</p><p>To assess the effects of soil moisture on isoprene emissions and, lastly, on ozone levels in the Euro-Mediterranean region, we use the regional climate model RegCM4.7, coupled to the land surface model CLM4.5, MEGAN2.1 and a chemistry module (RegCM4.7chem-CLM4.5-MEGAN2.1). We have performed a control experiment over 1987-2016 (with a 5-yr spin-up) at a horizontal resolution of 0.22°. Model output from the control experiment is used to initialize RegCM4.7chem-CLM4.5-MEGAN2.1 for the 10 most dry/wet summers (May-August) selected referring to the 1970-2016 precipitation climatology. Each May-August experiment is run with the old and with the new MEGAN soil moisture activity factor γ<sub>sm</sub>.  The results are then compared with a simulation whit no soil moisture activity factor. Both activity factors γ<sub>sm</sub> reduce isoprene emissions under water deficit.</p><p>During dry summers, the old soil moisture activity factor reduces isoprene emissions homogeneously over the model domain by nearly 100%, while ozone levels decrease by around 10%. When the new γ<sub>sm </sub>is used,<sub></sub>isoprene emissions are reduced with a patchy pattern by 10-20%, while ground-surface ozone levels diminish homogeneously by few percent over the southern part of the model domain.</p>

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.

Evaluation of the regional climate model RegCM4.7 over the Carpathian region for very wet and average years

2020 ◽  
Author(s):  
Tímea Kalmár ◽  
Ildikó Pieczka ◽  
Rita Pongrácz
Keyword(s):  
Soil Moisture ◽  
Regional Climate Model ◽  
Land Surface ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Cloud Microphysics ◽  
Mountainous Area ◽  
Model Version ◽  
Carpathian Region

<p>Precipitation is one of the most important climate variables in many aspects due to its key impact on agriculture, water management, etc. However, it remains a challenge for climate models to realistically simulate the regional patterns, temporal variations, and intensity of precipitation. The difficulty arises from the complexity of precipitation processes within the atmosphere stemming from cloud microphysics, cumulus convection, large-scale circulations, planetary boundary layer (PBL) processes, and many others. This is especially true for heterogeneous surfaces with complex orography such as the Carpathian region.  Thus, the Carpathian Basin, with its surrounding mountains, requires higher model resolution, along with different parameterizations, compared to more homogenous regions. The aim of the study is to reproduce the historical precipitation pattern through testing the parameterization of surface processes. The appropriate representations of land surface component in climate models are essential for the simulation of surface and subsurface runoff, soil moisture, and evapotranspiration. Furthermore, PBL strongly influences temperature, moisture, and wind through the turbulent transfer of air mass. The current study focuses on the newest model version of RegCM (RegCM4.7), with which we carry out simulations using different parameterization schemes over the Carpathian region. We investigate the effects of land-surface schemes (i.e. BATS - Biosphere-Atmosphere Transfer Scheme and CLM4.5 - Community Land Model version 4.5) in the regional climate model. Studies over different regions have shown that CLM offers improvements in terms of land–atmosphere exchanges of moisture and energy and associated surface climate feedbacks compared with BATS. Our aim includes evaluating whether this is the case for the Carpathian region.</p><p>Four 1-year-long experiments both for 1981 and 2010 (excluding the spin-up time) are completed using the same domain, initial and lateral atmospheric boundary data conditions (i.e. ERA-Interim), with a 10 km spatial resolution. These years were chosen because 1981 was a normal year in terms of precipitation, while 2010 was the wettest year in Hungary from the beginning of the 20th century. We carry out a detailed analysis of RegCM outputs focusing not only on standard climatological variables (precipitation and temperature), but also on additional meteorological variables, which have important roles in the water cycle (e.g. soil moisture, evapotranspiration). The simulations are compared with the CARPATCLIM observed, homogenised, gridded dataset and other databases (ESA CCI Soil Moisture Product New Version Release (v04.5) and Surface Solar Radiation Data Set - Heliosat (SARAH)). It is found that the simulated near-surface temperature and precipitation are better represented in the CLM scheme than in the BATS when compared with observations, both over the lowland and mountainous area. The model simulations also show that the precipitation is overestimated more over mountainous area in 2010 than in 1981.  </p>

scholarly journals Results from a full coupling of the HIRHAM regional climate model and the MIKE SHE hydrological model for a Danish catchment

2014 ◽  
Vol 18 (11) ◽  
pp. 4733-4749 ◽  
Author(s):  
M. A. D. Larsen ◽  
J. C. Refsgaard ◽  
M. Drews ◽  
M. B. Butts ◽  
K. H. Jensen ◽  
...  
Keyword(s):  
Land Surface ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Coupled Model ◽  
Research Field ◽  
Land Surface Models ◽  
Mike She ◽  
Simulated Precipitation ◽  
Surface Models

Abstract. A major challenge in the emerging research field of coupling of existing regional climate models (RCMs) and hydrology/land-surface models is the computational interaction between the models. Here we present results from a full two-way coupling of the HIRHAM RCM over a 4000 km × 2800 km domain at 11 km resolution and the combined MIKE SHE-SWET hydrology and land-surface models over the 2500 km2 Skjern River catchment. A total of 26 one-year runs were performed to assess the influence of the data transfer interval (DTI) between the two models and the internal HIRHAM model variability of 10 variables. DTI frequencies between 12 and 120 min were assessed, where the computational overhead was found to increase substantially with increasing exchange frequency. In terms of hourly and daily performance statistics the coupled model simulations performed less accurately than the uncoupled simulations, whereas for longer-term cumulative precipitation the opposite was found, especially for more frequent DTI rates. Four of six output variables from HIRHAM, precipitation, relative humidity, wind speed and air temperature, showed statistically significant improvements in root-mean-square error (RMSE) by reducing the DTI. For these four variables, the HIRHAM RMSE variability corresponded to approximately half of the influence from the DTI frequency and the variability resulted in a large spread in simulated precipitation. Conversely, DTI was found to have only a limited impact on the energy fluxes and discharge simulated by MIKE SHE.

scholarly journals On the utility of land surface models for agricultural drought monitoring

2012 ◽  
Vol 16 (9) ◽  
pp. 3451-3460 ◽  
Author(s):  
W. T. Crow ◽  
S. V. Kumar ◽  
J. D. Bolten
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Cross Correlation ◽  
Root Zone ◽  
Vegetation Indices ◽  
Agricultural Drought ◽  
Drought Monitoring ◽  
Global Evaluation ◽  
Land Surface Models ◽  
Surface Models

Abstract. The lagged rank cross-correlation between model-derived root-zone soil moisture estimates and remotely sensed vegetation indices (VI) is examined between January 2000 and December 2010 to quantify the skill of various soil moisture models for agricultural drought monitoring. Examined modeling strategies range from a simple antecedent precipitation index to the application of modern land surface models (LSMs) based on complex water and energy balance formulations. A quasi-global evaluation of lagged VI/soil moisture cross-correlation suggests, when globally averaged across the entire annual cycle, soil moisture estimates obtained from complex LSMs provide little added skill (< 5% in relative terms) in anticipating variations in vegetation condition relative to a simplified water accounting procedure based solely on observed precipitation. However, larger amounts of added skill (5–15% in relative terms) can be identified when focusing exclusively on the extra-tropical growing season and/or utilizing soil moisture values acquired by averaging across a multi-model ensemble.

scholarly journals The effect of climate type on timescales of drought propagation in an ensemble of global hydrological models

2018 ◽  
Vol 22 (9) ◽  
pp. 4649-4665 ◽  
Author(s):  
Anouk I. Gevaert ◽  
Ted I. E. Veldkamp ◽  
Philip J. Ward
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Spatial Scales ◽  
Standardized Precipitation Index ◽  
Meteorological Drought ◽  
Hydrological Drought ◽  
Propagation Time ◽  
Hydrological Models ◽  
Land Surface Models ◽  
Surface Models

Abstract. Drought is a natural hazard that occurs at many temporal and spatial scales and has severe environmental and socioeconomic impacts across the globe. The impacts of drought change as drought evolves from precipitation deficits to deficits in soil moisture or streamflow. Here, we quantified the time taken for drought to propagate from meteorological drought to soil moisture drought and from meteorological drought to hydrological drought. We did this by cross-correlating the Standardized Precipitation Index (SPI) against standardized indices (SIs) of soil moisture, runoff, and streamflow from an ensemble of global hydrological models (GHMs) forced by a consistent meteorological dataset. Drought propagation is strongly related to climate types, occurring at sub-seasonal timescales in tropical climates and at up to multi-annual timescales in continental and arid climates. Winter droughts are usually related to longer SPI accumulation periods than summer droughts, especially in continental and tropical savanna climates. The difference between the seasons is likely due to winter snow cover in the former and distinct wet and dry seasons in the latter. Model structure appears to play an important role in model variability, as drought propagation to soil moisture drought is slower in land surface models (LSMs) than in global hydrological models, but propagation to hydrological drought is faster in land surface models than in global hydrological models. The propagation time from SPI to hydrological drought in the models was evaluated against observed data at 127 in situ streamflow stations. On average, errors between observed and modeled drought propagation timescales are small and the model ensemble mean is preferred over the use of a single model. Nevertheless, there is ample opportunity for improvement as substantial differences in drought propagation are found at 10 % of the study sites. A better understanding and representation of drought propagation in models may help improve seasonal drought forecasting as well as constrain drought variability under future climate scenarios.

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