Land-atmosphere coupling during compound extreme heat and drought events in the LUCAS experiment: a new coupling metric for climate extremes

2021 ◽  
Author(s):  
Rita M. Cardoso ◽  
Daniela D. C. A. Lima ◽  
Pedro M. M. Soares ◽  
Diana Rechid ◽  
Marcus Breil ◽  
...  
Keyword(s):  
Land Use ◽  
Heat Flux ◽  
Soil Moisture ◽  
Land Cover ◽  
Latent Heat ◽  
Latent Heat Flux ◽  
Heat Waves ◽  
Extreme Heat ◽  
Positive Temperature ◽  
The Impact

<p>Land-atmosphere energy and water exchanges are fundamentally linked to soil-moisture. The distribution of the planets’ biomes hinges on the surface-atmosphere coupling since soil moisture and temperature feedbacks have a strong influence on plant transpiration and photosynthesis. Land use/land cover changes (LUC) modify locally land surface properties that control the land-atmosphere mass, energy, and momentum exchanges. The impact of these changes depends on the scale and nature of land cover modifications and is very difficult to quantify. However, large inconsistencies in the LUC impacts are observed between models, highlighting the need for common LUC across a large ensemble of models. The Flagship Pilot Study LUCAS (Land Use & Climate Across Scales) provides a coordinated effort to study LUC using an ensemble of regional climate models (RCMs). In the first phase of the project 3 experiments were performed for continental Europe: EVAL (current climate); GRASS (trees replaced by grassland) and FOREST (grasses and shrubs replaced by trees).  An analysis of the energy and moisture balance for the three experiments is performed, focusing on the relationship between the fluxes partitioning, heat waves and droughts. To better asses the link between extreme temperatures and soil moisture or evapotranspiration, a new coupling metric for short time scales is proposed, the Latent Heat Flux-Temperature Coupling Magnitude (LETCM). This new metric is computed for a specific period, considering the positive temperature extremes and the negative latent heat flux extremes. Areas with positive magnitude values imply higher temperature anomaly, due to a negative latent heat flux anomaly. This new metric only considers periods of strong coupling, with positive signals in areas of high temperatures and evaporative stress, allowing for the detection of events that are extreme for energy and water cycle. Concurrently, a new decile based normalised drought index is used to examine the concurrent heat extremes and droughts. The analysis focuses on the three experiments revealing that the number, amplitude and spatial distribution of compound extreme heat and drought is highly model dependant. The impact of afforestation or deforestation is not consistent across models.</p><p><strong>Acknowledgements</strong></p><p> The authors wish to acknowledge project LEADING (PTDC/CTA-MET/28914/2017) and FCT - project UIDB/50019/2020 - Instituto Dom Luiz.</p>

Evaluating Error Propagation in Coupled Land–Atmosphere Models

2011 ◽  
Vol 15 (28) ◽  
pp. 1-25 ◽  
Author(s):  
Bryan Pijanowski ◽  
Nathan Moore ◽  
Dasaraden Mauree ◽  
Dev Niyogi
Keyword(s):  
Land Use ◽  
Heat Flux ◽  
Soil Moisture ◽  
Land Cover ◽  
Surface Temperature ◽  
Latent Heat ◽  
Latent Heat Flux ◽  
Error Propagation ◽  
Change Model ◽  
Climate Variables

Abstract This study examines how land-use errors from the Land Transformation Model (LTM) propagate through to climate as simulated by the Regional Atmospheric Model System (RAMS). The authors conducted five simulations of regional climate over East Africa: one using observed land cover/land use (LULC) and four utilizing LTM-derived LULC. The study examined how quantifiable errors generated by the LTM impact typical land–climate variables: precipitation, land surface temperature, air temperature, soil moisture, and latent heat flux. Error propagation was not evident when domain averages for the land–climate variables of the yearlong simulation were examined. However, the authors found that spatial errors from the LTM propagate through in complex ways, temporally affecting the seasonal distributions of rainfall, surface temperature, soil moisture, and latent heat flux. In particular, rainy seasons exhibited greater precipitation in LTM-RAMS simulations than in the reference simulation and less precipitation occurred during the dry season. Complex interactions of precipitation and soil moisture were also evident. Overall, results indicate that small errors from a land change model could grow as a “coupling drift” if both are used to forecast into the future; these couplings could create larger combined errors of land–climate interactions because of time-scale differences into the future. Thus, although land-use change projection is necessary for a more accurate climate projection, existing errors from a land change model will likely amplify through the climate simulation. This finding affects interpretation of large-scale versus land-use/land-cover feedbacks on climate projections.

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.

The Impact of Vertical Measurement Depth on the Information Content of Soil Moisture for Latent Heat Flux Estimation

2016 ◽  
Vol 17 (9) ◽  
pp. 2419-2430 ◽  
Author(s):  
Jianxiu Qiu ◽  
Wade T. Crow ◽  
Grey S. Nearing
Keyword(s):  
Heat Flux ◽  
Time Series ◽  
Soil Moisture ◽  
Information Content ◽  
Latent Heat ◽  
Latent Heat Flux ◽  
Heat Flux Estimation ◽  
Vertical Support ◽  
Flux Estimation ◽  
The Impact

Abstract This study aims to identify the impact of vertical support on the information content of soil moisture (SM) for latent heat flux estimation. This objective is achieved via calculation of the mutual information (MI) content between multiple soil moisture variables (with different vertical supports) and current/future evaporative fraction (EF) using ground-based soil moisture and latent/sensible heat flux observations acquired from the AmeriFlux network within the contiguous United States. Through the intercomparison of MI results from different SM–EF pairs, the general value (for latent heat flux estimation) of superficial soil moisture observations , vertically integrated soil moisture observations , and vertically extrapolated soil moisture time series [soil wetness index (SWI) from a simple low-pass transformation of ] are examined. Results suggest that, contrary to expectations, 2-day averages of and have comparable mutual information with regards to EF. That is, there is no clear evidence that the information content for flux estimation is enhanced via deepening the vertical support of superficial soil moisture observations. In addition, the utility of SWI in monitoring and forecasting EF is partially dependent on the adopted parameterization of time-scale parameter T in the exponential filter. Similar results are obtained when analyses are conducted at the monthly time scale, only with larger error bars. The contrast between the results of this paper and past work focusing on utilizing soil moisture to predict vegetation condition demonstrates that the particular application should be considered when characterizing the information content of soil moisture time series measurements.

scholarly journals Impact of Deforestation on Land–Atmosphere Coupling Strength and Climate in Southeast Asia

2020 ◽  
Vol 12 (15) ◽  
pp. 6140
Author(s):  
Merja H. Tölle
Keyword(s):  
Heat Flux ◽  
Land Cover ◽  
Southeast Asia ◽  
Latent Heat ◽  
Latent Heat Flux ◽  
Coupling Strength ◽  
El Niño ◽  
El Nino ◽  
La Nina ◽  
The Impact

Southeast Asia (SEA) is a deforestation hotspot. A thorough understanding of the accompanying biogeophysical consequences is crucial for sustainable future development of the region’s ecosystem functions and society. In this study, data from ERA-Interim driven simulations conducted with the state-of-the-art regional climate model COSMO-CLM (CCLM; version 4.8.17) at 14 km horizontal resolution are analyzed over SEA for the period from 1990 to 2004, and during El Niño–Southern Oscillation (ENSO) events for November to March. A simulation with large-scale deforested land cover is compared to a simulation with no land cover change. In order to attribute the differences due to deforestation to feedback mechanisms, the coupling strength concept is applied based on Pearson correlation coefficients. The correlations were calculated based on 10-day means between the latent heat flux and maximum temperature, the latent and sensible heat flux, and the latent heat flux and planetary boundary layer height. The results show that the coupling strength between land and atmosphere increased for all correlations due to deforestation. This implies a strong impact of the land on the atmosphere after deforestation. Differences in environmental conditions due to deforestation are most effective during La Niña years. The strength of La Nina events on the region is reduced as the impact of deforestation on the atmosphere with drier and warmer conditions superimpose this effect. The correlation strength also intensified and shifted towards stronger coupling during El Niño events for both Control and Grass simulations. However, El Niño years have the potential to become even warmer and drier than during usual conditions without deforestation. This could favor an increase in the formation of tropical cyclones. Whether deforestation will lead to a permanent transition to agricultural production increases in this region cannot be concluded. Rather, the impact of deforestation will be an additional threat besides global warming in the next decades due to the increase in the occurrence of multiple extreme events. This may change the type and severity of upcoming impacts and the vulnerability and sustainability of our society.

scholarly journals Simulation of Land Surface Climate over China with RegCM4.5: Verification and Analysis

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Minghao Yang ◽  
Ruiting Zuo ◽  
Liqiong Wang ◽  
Xiong Chen
Keyword(s):  
Heat Flux ◽  
Soil Moisture ◽  
Surface Temperature ◽  
Latent Heat ◽  
Latent Heat Flux ◽  
Land Surface Temperature ◽  
South China ◽  
Land Surface ◽  
Reanalysis Data ◽  
Surface Evaporation

The ability of RegCM4.5 using land surface scheme CLM4.5 to simulate the physical variables related to land surface state was investigated. The NCEP-NCAR reanalysis data for the period 1964–2003 were used to drive RegCM4.5 to simulate the land surface temperature, precipitation, soil moisture, latent heat flux, and surface evaporation. Based on observations and reanalysis data, a few land surface variables were analyzed over China. The results showed that some seasonal features of land surface temperature in summer and winter as well as its magnitude could be simulated well. The simulation of precipitation was sensitive to region and season. The model could, to a certain degree, simulate the seasonal migration of rainband in East China. The overall spatial distribution of the simulated soil moisture was better in winter than in summer. The simulation of latent heat flux was also better in winter. In summer, the latent heat flux bias mainly arose from surface evaporation bias in Northwest China, and it primarily arose from vegetation evapotranspiration bias in South China. In addition, the large latent heat flux bias in South China during summer was probably due to less precipitation generated in the model and poor representation of vegetation cover in this region.

Studies on lichen-dominated systems. XX. An examination of some aspects of the northern boreal lichen woodlands in Canada

1977 ◽  
Vol 55 (4) ◽  
pp. 393-410 ◽  
Author(s):  
K. A. Kershaw
Keyword(s):  
Heat Flux ◽  
Soil Moisture ◽  
Latent Heat ◽  
Latent Heat Flux ◽  
Higher Plants ◽  
Sensible Heat Flux ◽  
Subsequent Development ◽  
Sensible Heat ◽  
Early Recovery ◽  
Lichen Woodland

The existence of two major types of lichen woodland in Canada, Cladonia stellaris woodland and Stereocaulon paschale woodland, is discussed in relation to their seral nature and their rarely developed theoretical climax type.Our own observations, coupled with previous descriptions from a wider area, suggest that Stereocaulon paschale woodland replaces Cladonia stellaris woodland in a more or less continuous zone from just west of Churchill across to Great Slave Lake, immediately north and south of latitude 60° N. Both woodland types are often typical of sandy soils (pH 6 or less) and almost always represent the final recovery phase after fire. Rarely, the lichen surface is replaced by a continuous moss cover as the spruce canopy closes. The lichen surface is thus dependent on the lack of competition from higher plants, the absence of which is characteristic of the climate of this northern boreal region. Cladonia stellaris woodland also occurs on palsas and peat plateaux where, again, lack of higher plant competition and a suitable pH exist.The recovery sequence after fire is a highly complex process and as yet only the following parameters have been categorized. In the early recovery phases, limited soil moisture and hence a reduced summer latent heat flux enhance the sensible heat flux. The surface conditions are analogous to those of a hot desert with very high surface temperatures and extremely large diurnal temperature fluctuations. The physiology of these initial moss and lichen colonizers presumably enables them to tolerate these harsh conditions. The establishment of a few spruce seedlings and the subsequent development of open lichen woodland modulates the harsh summer temperature regime and allows the further development of a vegetated surface. After humus accumulation, which acts as an effective mulch, summer soil moisture is elevated, enhancing the latent heat flux and correspondingly reducing the sensible heat flux. This probably allows the full development of mature lichen woodland with its almost monospecific ground cover of either Cladonia stellaris or Stereocaulon paschale. Limited data suggest that the net photosynthetic responses of these two species is favoured by the relatively warm mesic conditions established by the open spruce canopy. Good accumulation of snow in the winter is probably also important for protection of the lichen surface from low temperatures. The open nature of mature lichen woodland is apparently maintained by an active inhibition of spruce seedling establishment by the lichen mat, although the mechanism is not entirely clear.

scholarly journals Mesoscale Simulations of the Land Surface Effects of Historical Logging in a Moist Continental Climate Regime

2008 ◽  
Vol 47 (8) ◽  
pp. 2166-2182 ◽  
Author(s):  
Nicholas P. Klingaman ◽  
Jason Butke ◽  
Daniel J. Leathers ◽  
Kevin R. Brinson ◽  
Elsa Nickl
Keyword(s):  
Climate Change ◽  
Heat Flux ◽  
Land Cover ◽  
Latent Heat ◽  
Latent Heat Flux ◽  
Land Surface ◽  
Climate Regime ◽  
Clear Cut ◽  
Atmosphere System ◽  
Regional Climates

Abstract An enhanced knowledge of the feedbacks from land surface changes on regional climates is of great importance in the attribution of climate change. To explore the effects of deforestation on a midlatitude climate regime, two sets of two five-member ensembles of 28-day simulations were conducted using the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) coupled to the “Noah” land surface model. The four ensembles represented conditions in summer (August) and winter (February) across the northern mid-Atlantic United States before and after extensive late-nineteenth-century logging of hardwood forests in central and northern Pennsylvania. Prelogging ensembles prescribed a vegetative cover of an evergreen needleleaf forest; postlogging ensembles prescribed sparse vegetation and bare soil to simulate clear-cut deforestation. The results of the MM5 experiments showed a decided seasonality in the response of the land surface–atmosphere system to deforestation, with much stronger effects arising in summer. In August, deforestation caused a repartitioning of the surface energy budget, beginning with a decrease in the latent heat flux of more than 60 W m−2 across the land cover–forcing area, representing almost one-half of the latent heat flux under prelogging land cover. Concomitant with this decrease in evapotranspiration, mean 2-m air temperatures warmed by at least 1.5°C. Increases in sensible heat flux led to a 150-m mean increase in the height of the atmospheric boundary layer over the deforested area. Low-level atmospheric mixing ratios and total precipitation decreased under clear-cut conditions. Mean soil moisture increased in all model levels to 150 cm because of a decrease in vegetative uptake of water, except at the 5-cm level at which such decreases were effectively balanced by greater soil evaporation and less precipitation. A strong diurnal variation in the response to deforestation of ground and lower-atmosphere temperatures and heat fluxes was also identified for the summer season. The February simulations showed the effects of deforestation during low-insolation months to be small and variable. The strong response of the summer land surface–atmosphere system to deforestation shown here suggests that land cover changes can appreciably affect regional climates. Thus, the role of human-induced and naturally occurring land cover variability should not be ignored in the attribution of climate change.

scholarly journals Improving the representation of cropland sites in the Community Land Model (CLM) version 5.0

2020 ◽  
Author(s):  
Theresa Boas ◽  
Heye Bogena ◽  
Thomas Grünwald ◽  
Bernard Heinesch ◽  
Dongryeol Ryu ◽  
...  
Keyword(s):  
Land Use ◽  
Heat Flux ◽  
Winter Wheat ◽  
Latent Heat ◽  
Latent Heat Flux ◽  
Field Data ◽  
Energy Fluxes ◽  
Cash Crop ◽  
Cover Cropping ◽  
Community Land Model

Abstract. The incorporation of a comprehensive crop module in land surface models offers the possibility to study the effect of agricultural land use and land management changes on the terrestrial water, energy and biogeochemical cycles. It may help to improve the simulation of biogeophysical and biogeochemical processes on regional and global scales in the framework of climate and land use change. In this study, the performance of the crop module of the Community Land Model version 5 (CLM5) was evaluated at point scale with site specific field data focussing on the simulation of seasonal and inter-annual variations in crop growth, planting and harvesting cycles, and crop yields as well as water, energy and carbon fluxes. In order to better represent agricultural sites, the model was modified by (1) implementing the winter wheat subroutines after Lu et al. (2017) in CLM5; (2) implementing plant specific parameters for sugar beet, potatoes and winter wheat, thereby adding these crop functional types (CFT) to the list of actively managed crops in CLM5; (3) introducing a cover cropping subroutine that allows multiple crop types on the same column within one year. The latter modification allows the simulation of cropping during winter months before usual cash crop planting begins in spring, which is a common agricultural management technique in humid and sub-humid regions. We compared simulation results with field data and found that both the parameterization of the CFTs, as well as the winter wheat subroutines, led to a significant simulation improvement in terms of energy fluxes, leaf area index (LAI), net ecosystem exchange (RMSE reduction for latent and sensible heat by up to 57 % and 59 % respectively) and crop yield (up to 87 % improvement in winter wheat yield prediction) compared with default model results. The cover cropping subroutine yielded a substantial improvement in representation of field conditions after harvest of the main cash crop (winter season) in terms of LAI curve and latent heat flux (reduction of winter time RMSE for latent heat flux by 42 %). We anticipate that our model modifications offer opportunities to improve yield predictions, to study the effects of large-scale cover cropping on energy fluxes, soil carbon and nitrogen pools, and soil water storage in future studies with CLM5.

scholarly journals Propagating Subsurface Uncertainty to the Atmosphere Using Fully Coupled Stochastic Simulations

2011 ◽  
Vol 12 (4) ◽  
pp. 690-701 ◽  
Author(s):  
John L. Williams ◽  
Reed M. Maxwell
Keyword(s):  
Heat Flux ◽  
Soil Moisture ◽  
Wind Speed ◽  
Latent Heat ◽  
Latent Heat Flux ◽  
Hydrologic Model ◽  
Stochastic Simulations ◽  
Multiple Model ◽  
Conditional Monte Carlo ◽  
Fully Coupled

Abstract Feedbacks between the land surface and the atmosphere, manifested as mass and energy fluxes, are strongly correlated with soil moisture, making soil moisture an important factor in land–atmosphere interactions. It is shown that a reduction of the uncertainty in subsurface properties such as hydraulic conductivity (K) propagates into the atmosphere, resulting in a reduction in uncertainty in land–atmosphere feedbacks that yields more accurate atmospheric predictions. Using the fully coupled groundwater-to-atmosphere model ParFlow-WRF, which couples the hydrologic model ParFlow with the Weather Research and Forecasting (WRF) atmospheric model, responses in land–atmosphere feedbacks and wind patterns due to subsurface heterogeneity are simulated. Ensembles are generated by varying the spatial location of subsurface properties while maintaining the global statistics and correlation structure. This approach is common to the hydrologic sciences but uncommon in atmospheric simulations where ensemble forecasts are commonly generated with perturbed initial conditions or multiple model parameterizations. It is clearly shown that different realizations of K produce variation in soil moisture, latent heat flux, and wind for both point and domain-averaged quantities. Using a single random field to represent a control case, varying amounts of K data are sampled and subsurface data are incorporated into conditional Monte Carlo ensembles to show that the difference between the ensemble mean prediction and the control saturation, latent heat flux, and wind speed are reduced significantly via conditioning of K. By reducing uncertainty associated with land–atmosphere feedback mechanisms, uncertainty is also reduced in both spatially distributed and domain-averaged wind speed magnitudes, thus improving the ability to make more accurate forecasts, which is important for many applications such as wind energy.

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