Flux Replacement as a Method to Diagnose Coupled Land–Atmosphere Model Feedback

Abstract The potential role of the land surface state in improving predictions of seasonal climate is investigated with a coupled land–atmosphere climate model. Climate simulations for 18 boreal-summer seasons (1982–99) have been conducted with specified observed sea surface temperature (SST). The impact on prediction skill of the initial land surface state (interannually varying versus climatological soil wetness) and the effect of errors in downward surface fluxes (precipitation and longwave/shortwave radiation) over land are investigated with a number of parallel experiments. Flux errors are addressed by replacing the downward fluxes with observed values in various combinations to ascertain the separate roles of water and energy flux errors on land surface state variables, upward water and energy fluxes from the land surface, and the important climate variables of precipitation and near-surface air temperature. Large systematic errors are found in the model, which are only mildly alleviated by the specification of realistic initial soil wetness. The model shows little skill in simulating seasonal anomalies of precipitation, but it does have skill in simulating temperature variations. Replacement of the downward surface fluxes has a clear positive impact on systematic errors, suggesting that the land–atmosphere feedback is helping to exacerbate climate drift. Improvement in the simulation of year-to-year variations in climate is even more evident. With flux replacement, the climate model simulates temperature anomalies with considerable skill over nearly all land areas, and a large fraction of the globe shows significant skill in the simulation of precipitation anomalies. This suggests that the land surface can communicate climate anomalies back to the atmosphere, given proper meteorological forcing. Flux substitution appears to have the largest benefit to improving precipitation skill over the Northern Hemisphere midlatitudes, whereas use of realistic land surface initial conditions improves skill to significant levels over regions of the Southern Hemisphere. Correlations between sets of integrations show that the model has a robust and systematic global response to SST anomalies.

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The Land Surface Contribution to the Potential Predictability of Boreal Summer Season Climate

Journal of Hydrometeorology ◽

10.1175/jhm444.1 ◽

2005 ◽

Vol 6 (5) ◽

pp. 618-632 ◽

Cited By ~ 34

Author(s):

Paul A. Dirmeyer

Keyword(s):

Surface Temperature ◽

Land Surface ◽

Climate Model ◽

Initial Conditions ◽

Surface Fluxes ◽

Boreal Summer ◽

Potential Predictability ◽

Noise Component ◽

Near Surface ◽

Soil Wetness

Abstract The role of the land surface in contributing to the potential predictability of the boreal summer climate is investigated with a coupled land–atmosphere climate model. Ensemble simulations for 1982–99 have been conducted with specified observed sea surface temperatures (SSTs). Several treatments of the land surface are investigated: climatological land surface initialization, realistic initialization of soil wetness, and a series of experiments where downward surface fluxes over land are replaced with observed proxies of precipitation, shortwave, and longwave radiation. Without flux replacement the model exhibits strong drift in soil wetness and both systematic errors and poor simulation of interannual variations of precipitation and near-surface temperature. With flux replacement there are large improvements in simulation of both spatial patterns and interannual variability of precipitation and surface temperature. The land surface apparently does contribute, through positive feedback with the atmosphere, to regional climate anomalies. However, because of the sizeable noise component in precipitation, the strong land–atmosphere feedback may not translate into reliable enhancements in predictability, particularly in years of weak anomalies in the land surface initial conditions at the start of boreal summer.

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The Hydrologic Feedback Pathway for Land–Climate Coupling

Journal of Hydrometeorology ◽

10.1175/jhm526.1 ◽

2006 ◽

Vol 7 (5) ◽

pp. 857-867 ◽

Cited By ~ 47

Author(s):

Paul A. Dirmeyer

Keyword(s):

Land Surface ◽

Climate Model ◽

Water Cycle ◽

Initial Conditions ◽

Global Climate ◽

Global Climate Model ◽

Signal Transmission ◽

Hydrologic Cycle ◽

Boreal Summer ◽

The Impact

Abstract The impact of improvements in land surface initialization and specification of observed rainfall in global climate model simulations of boreal summer are examined to determine how the changes propagate around the hydrologic cycle in the coupled land–atmosphere system. On the global scale, about 70% of any imparted signal in the hydrologic cycle is lost in the transition from atmosphere to land, and 70% of the remaining signal is lost from land to atmosphere. This means that globally, less than 10% of the signal of any change survives the complete circuit of the hydrologic cycle in this model. Regionally, there is a great deal of variability. Specification of observed precipitation to the land component of the climate model strongly communicates its signal to soil wetness in rainy regions, but predictive skill in evapotranspiration arises primarily in dry regions. A maximum in signal transmission to model precipitation exists in between, peaking where mean rainfall rates are 1.5–2 mm day−1. It appears that the nature of the climate system inherently limits to these regions the potential impact on prediction of improvements in the ability of models to simulate the water cycle. Land initial conditions impart a weaker signal on the system than replacement of precipitation, so a weaker response is realized in the system, focused mainly in dry regions.

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Examining the Interaction of Growing Crops with Local Climate Using a Coupled Crop–Climate Model

Journal of Climate ◽

10.1175/2008jcli2494.1 ◽

2009 ◽

Vol 22 (6) ◽

pp. 1393-1411 ◽

Cited By ~ 34

Author(s):

Tom Osborne ◽

Julia Slingo ◽

David Lawrence ◽

Tim Wheeler

Keyword(s):

Climate Variability ◽

Crop Yield ◽

Land Surface ◽

Climate Models ◽

Climate Model ◽

Crop Growth ◽

Surface Fluxes ◽

Near Surface ◽

Local Climate ◽

The Impact

Abstract This paper examines to what extent crops and their environment should be viewed as a coupled system. Crop impact assessments currently use climate model output offline to drive process-based crop models. However, in regions where local climate is sensitive to land surface conditions more consistent assessments may be produced with the crop model embedded within the land surface scheme of the climate model. Using a recently developed coupled crop–climate model, the sensitivity of local climate, in particular climate variability, to climatically forced variations in crop growth throughout the tropics is examined by comparing climates simulated with dynamic and prescribed seasonal growth of croplands. Interannual variations in land surface properties associated with variations in crop growth and development were found to have significant impacts on near-surface fluxes and climate; for example, growing season temperature variability was increased by up to 40% by the inclusion of dynamic crops. The impact was greatest in dry years where the response of crop growth to soil moisture deficits enhanced the associated warming via a reduction in evaporation. Parts of the Sahel, India, Brazil, and southern Africa were identified where local climate variability is sensitive to variations in crop growth, and where crop yield is sensitive to variations in surface temperature. Therefore, offline seasonal forecasting methodologies in these regions may underestimate crop yield variability. The inclusion of dynamic crops also altered the mean climate of the humid tropics, highlighting the importance of including dynamical vegetation within climate models.

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Assessing the Impact of Changes in Land Surface Conditions on WRF Predictions in Arid Regions

Journal of Hydrometeorology ◽

10.1175/jhm-d-20-0083.1 ◽

2020 ◽

Vol 21 (12) ◽

pp. 2829-2853 ◽

Cited By ~ 1

Author(s):

Marouane Temimi ◽

Ricardo Fonseca ◽

Narendra Nelli ◽

Michael Weston ◽

Mohan Thota ◽

...

Keyword(s):

Eddy Covariance ◽

Soil Texture ◽

Land Surface ◽

Surface Wind ◽

Surface Fluxes ◽

Abu Dhabi ◽

Horizontal Wind ◽

Cold Bias ◽

Near Surface ◽

The Impact

AbstractA thorough evaluation of the Weather Research and Forecasting (WRF) Model is conducted over the United Arab Emirates, for the period September 2017–August 2018. Two simulations are performed: one with the default model settings (control run), and another one (experiment) with an improved representation of soil texture and land use land cover (LULC). The model predictions are evaluated against observations at 35 weather stations, radiosonde profiles at the coastal Abu Dhabi International Airport, and surface fluxes from eddy-covariance measurements at the inland city of Al Ain. It is found that WRF’s cold temperature bias, also present in the forcing data and seen almost exclusively at night, is reduced when the surface and soil properties are updated, by as much as 3.5 K. This arises from the expansion of the urban areas, and the replacement of loamy regions with sand, which has a higher thermal inertia. However, the model continues to overestimate the strength of the near-surface wind at all stations and seasons, typically by 0.5–1.5 m s−1. It is concluded that the albedo of barren/sparsely vegetated regions in WRF (0.380) is higher than that inferred from eddy-covariance observations (0.340), which can also explain the referred cold bias. At the Abu Dhabi site, even though soil texture and LULC are not changed, there is a small but positive effect on the predicted vertical profiles of temperature, humidity, and horizontal wind speed, mostly between 950 and 750 hPa, possibly because of differences in vertical mixing.

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The GLACE-Hydrology Experiment: Effects of Land–Atmosphere Coupling on Soil Moisture Variability and Predictability

Journal of Climate ◽

10.1175/jcli-d-19-0598.1 ◽

2020 ◽

Vol 33 (15) ◽

pp. 6511-6529

Author(s):

Sanjiv Kumar ◽

Matthew Newman ◽

David M. Lawrence ◽

Min-Hui Lo ◽

Sathish Akula ◽

...

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Radiative Forcing ◽

Climate Model ◽

Signal To Noise Ratio ◽

Boreal Summer ◽

Runoff Variability ◽

The Impact ◽

Soil Moisture Variability ◽

Moisture Variability

AbstractThe impact of land–atmosphere anomaly coupling on land variability is investigated using a new two-stage climate model experimental design called the “GLACE-Hydrology” experiment. First, as in the GLACE-CMIP5 experiment, twin sets of coupled land–atmosphere climate model (CAM5-CLM4.5) ensembles are performed, with each simulation using the same prescribed observed sea surface temperatures and radiative forcing for the years 1971–2014. In one set, land–atmosphere anomaly coupling is removed by prescribing soil moisture to follow the control model’s seasonally evolving soil moisture climatology (“land–atmosphere uncoupled”), enabling a contrast with the original control set (“land–atmosphere coupled”). Then, the atmospheric outputs from both sets of simulations are used to force land-only ensemble simulations, allowing investigation of the resulting soil moisture variability and memory under both the coupled and uncoupled scenarios. This study finds that in midlatitudes during boreal summer, land–atmosphere anomaly coupling significantly strengthens the relationship between soil moisture and evapotranspiration anomalies, both in amplitude and phase. This allows for decreased moisture exchange between the land surface and atmosphere, increasing soil moisture memory and often its variability as well. Additionally, land–atmosphere anomaly coupling impacts runoff variability, especially in wet and transition regions, and precipitation variability, although the latter has surprisingly localized impacts on soil moisture variability. As a result of these changes, there is an increase in the signal-to-noise ratio, and thereby the potential seasonal predictability, of SST-forced hydroclimate anomalies in many areas of the globe, especially in the midlatitudes. This predictability increase is greater for soil moisture than precipitation and has important implications for the prediction of drought.

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Sensitivity of Land Surface Simulations to the Treatment of Vegetation Properties and the Implications for Seasonal Climate Prediction

Journal of Hydrometeorology ◽

10.1175/2007jhm931.1 ◽

2008 ◽

Vol 9 (3) ◽

pp. 348-366 ◽

Cited By ~ 7

Author(s):

Xiang Gao ◽

Paul A. Dirmeyer ◽

Zhichang Guo ◽

Mei Zhao

Keyword(s):

Land Surface ◽

Climate Model ◽

Positive Impact ◽

Land Surface Model ◽

Surface Fluxes ◽

Surface Model ◽

Canopy Interception ◽

Area Index ◽

Temperature And Precipitation ◽

The Impact

Abstract A coupled land–atmosphere climate model is used to investigate the impact of vegetation parameters (leaf area index, absorbed radiation, and greenness fraction) on the simulation of surface fluxes and their potential role in improving climate forecasts. Ensemble simulations for 1986–95 have been conducted with specified observed sea surface temperatures. The vegetation impact is analyzed by comparing integrations with two different ways of specifying vegetation boundary conditions: observed interannually varying vegetation versus the climatological annual cycle. Parallel integrations are also implemented and analyzed for the land surface model in an uncoupled mode within the framework of the Second Global Soil Wetness Project (GSWP-2) for the same period. The sensitivity to vegetation anomalies in the coupled simulations appears to be relatively small. There appears to be only episodic and localized favorable impacts of vegetation variations on the skill of precipitation and temperature simulations. Impacts are sometimes manifested strictly through changes in land surface fluxes, and in other cases involve clear interactions with atmospheric processes. In general, interannual variations of vegetation tend to increase the temporal variability of radiation fluxes, soil evaporation, and canopy interception loss in terms of both spatial frequency and global mean. Over cohesive regions of significant and persistent vegetation anomalies, cumulative statistics do show a net response of surface fluxes, temperature, and precipitation with vegetation anomalies of ±20% corresponding to a precipitation response of about ±6%. However, in about half of these cases no significant response was found. The results presented here suggest that vegetation may be a useful element of the land surface for enhancing seasonal predictability, but its role in this model appears to be relatively minor. Improvement does not occur in all circumstances, and strong anomalies have the best chance of a positive impact on the simulation.

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Impact of Land Model Calibration on Coupled Land–Atmosphere Prediction

Journal of Hydrometeorology ◽

10.1175/jhm-d-12-0127.1 ◽

2013 ◽

Vol 14 (5) ◽

pp. 1373-1400 ◽

Cited By ~ 28

Author(s):

Joseph A. Santanello ◽

Sujay V. Kumar ◽

Christa D. Peters-Lidard ◽

Ken Harrison ◽

Shujia Zhou

Keyword(s):

Land Surface ◽

Initial Conditions ◽

Critical Role ◽

Land Surface Model ◽

Coupled Model ◽

Surface States ◽

Surface Model ◽

Near Surface ◽

Heat And Moisture ◽

The Impact

Abstract Land–atmosphere (LA) interactions play a critical role in determining the diurnal evolution of both planetary boundary layer (PBL) and land surface heat and moisture budgets, as well as controlling feedbacks with clouds and precipitation that lead to the persistence of dry and wet regimes. In this study, the authors examine the impact of improved specification of land surface states, anomalies, and fluxes on coupled Weather Research and Forecasting Model (WRF) forecasts during the summers of extreme dry (2006) and wet (2007) land surface conditions in the U.S. southern Great Plains. The improved land initialization and surface flux parameterizations are obtained through calibration of the Noah land surface model using the new optimization and uncertainty estimation subsystems in NASA's Land Information System (LIS-OPT/LIS-UE). The impact of the calibration on the 1) spinup of the land surface used as initial conditions and 2) the simulated heat and moisture states and fluxes of the coupled WRF simulations is then assessed. In addition, the sensitivity of this approach to the period of calibration (dry, wet, or average) is investigated. Results show that the offline calibration is successful in providing improved initial conditions and land surface physics for the coupled simulations and in turn leads to systematic improvements in land–PBL fluxes and near-surface temperature and humidity forecasts. Impacts are larger during dry regimes, but calibration during either primarily wet or dry periods leads to improvements in coupled simulations due to the reduction in land surface model bias. Overall, these results provide guidance on the questions of what, how, and when to calibrate land surface models for coupled model prediction.

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The Impact of Finer-Resolution Air–Sea Coupling on the Intraseasonal Oscillation of the Indian Monsoon

Journal of Climate ◽

10.1175/2010jcli3868.1 ◽

2011 ◽

Vol 24 (10) ◽

pp. 2451-2468 ◽

Cited By ~ 38

Author(s):

Nicholas P. Klingaman ◽

Steven J. Woolnough ◽

Hilary Weller ◽

Julia M. Slingo

Keyword(s):

Atmospheric Stability ◽

Intraseasonal Oscillation ◽

Boreal Summer ◽

Upper Ocean ◽

Vertical Resolution ◽

Coupled Models ◽

Diurnal Variability ◽

Near Surface ◽

The Impact ◽

Coupling Frequency

Abstract A newly assembled atmosphere–ocean coupled model, called HadKPP, is described and then used to determine the effects of subdaily air–sea coupling and fine near-surface ocean vertical resolution on the representation of the Northern Hemisphere summer intraseasonal oscillation. HadKPP comprises the Hadley Centre atmospheric model coupled to the K-Profile Parameterization ocean boundary layer model. Four 30-member ensembles were performed that vary in ocean vertical resolution between 1 and 10 m and in coupling frequency between 3 and 24 h. The 10-m, 24-h ensemble exhibited roughly 60% of the observed 30–50-day variability in sea surface temperatures and rainfall and very weak northward propagation. Enhancing only the vertical resolution or only the coupling frequency produced modest improvements in variability and just a standing intraseasonal oscillation. Only the 1-m, 3-h configuration generated organized, northward-propagating convection similar to observations. Subdaily surface forcing produced stronger upper-ocean temperature anomalies in quadrature with anomalous convection, which likely affected lower-atmospheric stability ahead of the convection, causing propagation. Well-resolved air–sea coupling did not improve the eastward propagation of the boreal summer intraseasonal oscillation in this model. Upper-ocean vertical mixing and diurnal variability in coupled models must be improved to accurately resolve and simulate tropical subseasonal variability. In HadKPP, the mere presence of air–sea coupling was not sufficient to generate an intraseasonal oscillation resembling observations.

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Evaluating the role of soil physical properties on simulated land-atmosphere interactions over South Africa using coupled atmosphere-hydrological modeling

10.5194/egusphere-egu21-9418 ◽

2021 ◽

Author(s):

Zhenyu Zhang ◽

Patrick Laux ◽

Joël Arnault ◽

Jianhui Wei ◽

Jussi Baade ◽

...

Keyword(s):

South Africa ◽

Physical Properties ◽

Land Degradation ◽

Land Surface ◽

Soil Physical Properties ◽

Near Surface ◽

Soil Database ◽

Global Soil ◽

The Impact

<p>Land degradation with its direct impact on vegetation, surface soil layers and land surface albedo, has great relevance with the climate system. Assessing the climatic and ecological effects induced by land degradation requires a precise understanding of the interaction between the land surface and atmosphere. In coupled land-atmosphere modeling, the low boundary conditions impact the thermal and hydraulic exchanges at the land surface, therefore regulates the overlying atmosphere by land-atmosphere feedback processes. However, those land-atmosphere interactions are not convincingly represented in coupled land-atmosphere modeling applications. It is partly due to an approximate representation of hydrological processes in land surface modeling. Another source of uncertainties relates to the generalization of soil physical properties in the modeling system. This study focuses on the role of the prescribed physical properties of soil in high-resolution land surface-atmosphere simulations over South Africa. The model used here is the hydrologically-enhanced Weather Research and Forecasting (WRF-Hydro) model. Four commonly used global soil datasets obtained from UN Food and Agriculture Organization (FAO) soil database, Harmonized World Soil Database (HWSD), Global Soil Dataset for Earth System Model (GSDE), and SoilGrids dataset, are incorporated within the WRF-Hydro experiments for investigating the impact of soil information on land-atmosphere interactions. The simulation results of near-surface temperature, skin temperature, and surface energy fluxes are presented and compared to observational-based reference dataset. It is found that simulated soil moisture is largely influenced by soil texture features, which affects its feedback to the atmosphere.</p>

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Dome effect of black carbon and its key influencing factors: A one-dimensional modelling study

10.5194/acp-2017-967 ◽

2017 ◽

Author(s):

Zilin Wang ◽

Xin Huang ◽

Aijun Ding

Keyword(s):

Boundary Layer ◽

Air Pollution ◽

Black Carbon ◽

Rural Areas ◽

Land Surface ◽

Critical Role ◽

Aerosol Layer ◽

Near Surface ◽

Haze Pollution ◽

The Impact

Abstract. Black carbon (BC) has been identified to play a critical role in aerosol-planet boundary layer (PBL) interaction and further deterioration of near-surface air pollution in megacities, which has been named as its dome effect. However, the impacts of key factors that influence this effect, such as the vertical distribution and aging processes of BC, and also the underlying land surface, have not been quantitatively explored yet. Here, based on available in-situ measurements of meteorology and atmospheric aerosols together with the meteorology-chemistry online coupled model, WRF-Chem, we conduct a set of parallel simulations to quantify the roles of these factors in influencing the BC's dome effect and surface haze pollution, and discuss the main implications of the results to air pollution mitigation in China. We found that the impact of BC on PBL is very sensitive to the altitude of aerosol layer. The upper level BC, especially those near the capping inversion, is more essential in suppressing the PBL height and weakening the turbulence mixing. The dome effect of BC tends to be significantly intensified as BC aerosol mixed with scattering aerosols during winter haze events, resulting in a decrease of PBL height by more than 25 %. In addition, the dome effect is more substantial (up to 15 %) in rural areas than that in the urban areas with the same BC loading, indicating an unexpected regional impact of such kind of effect to air quality in countryside. This study suggests that China's regional air pollution would greatly benefit from BC emission reductions, especially those from the elevated sources from the chimneys and also the domestic combustions in rural areas, through weakening the aerosol-boundary layer interactions that triggered by BC.

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