Land-atmosphere interactions and agricultural climate impacts

2020 ◽  
Author(s):  
Nathan Mueller
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Crop Yields ◽  
Extreme Temperature ◽  
Climate Impact ◽  
Climate Impacts ◽  
Temperature Trends ◽  
Crop Intensification ◽  
The Us ◽  
Earth System Models

<p>Agricultural climate impact projections routinely rely upon temperature-based statistical models to characterize historical variability and project future crop yields, and exposure to extremely hot temperatures is associated with severe crop losses. However, high temperatures over land can be strongly influenced by land surface conditions, including shifts in evapotranspiration arising from variations in vegetation productivity and soil moisture. This talk will highlight the ways in which such land-atmosphere interactions should be considered in agricultural climate impact assessments. I will show how crop intensification of both rainfed and irrigated production modified extreme temperature trends in the US and around the world. I will then show how the coupling between soil moisture and temperatures can bias climate impact projections based solely on temperature. Shifts in soil moisture-temperature coupling will be examined using earth system models.</p>

scholarly journals Combined measurement and modeling of the hydrological impact of hydraulic redistribution using CLM4.5 at eight AmeriFlux sites

2016 ◽  
Author(s):  
C. Fu ◽  
G. Wang ◽  
M. L. Goulden ◽  
R. L. Scott ◽  
K. Bible ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Hydraulic Redistribution ◽  
Energy Budgets ◽  
Soil Moisture Dynamics ◽  
Community Land Model ◽  
The Us ◽  
Hydrological Impact ◽  
The Impact ◽  
Moisture Dynamics

Abstract. Effects of hydraulic redistribution (HR) on hydrological, biogeochemical, and ecological processes have been demonstrated in the field, but the current generation of standard earth system models does not include a representation of HR. Though recent studies have examined the effect of incorporating HR into land surface models, few (if any) has tackled the magnitude of the HR flux itself or the soil moisture dynamics from which HR magnitude can be directly inferred. Here we incorporated Ryel et al.'s (2002) empirical equation describing HR into the NCAR Community Land Model Version 4.5 (CLM4.5), and examined the ability of the resulting hybrid model to capture the magnitude of HR flux and/or soil moisture dynamics from which HR can be directly inferred, to assess the impact of HR on surface water and energy budgets, and to explore how it may depend on climate regimes and vegetation conditions. Eight AmeriFlux sites characterized by contrasting climate regimes and multiple vegetation types were studied, including the US-Wrc Wind River Crane site in Washington State, the US-SRM Santa Rita Mesquite Savanna site in southern Arizona, and six sites along the Southern California Climate Gradient (US-SCs, g, f, w, c, and d). HR flux, evapotranspiration, and soil moisture were properly simulated in the present study, even in the face of various uncertainties. Our cross-ecosystem comparison showed that the timing, magnitude, and direction (upward or downward) of HR vary across ecosystems, and incorporation of HR into CLM4.5 improved the model-measurement match particularly during dry seasons. Our results also reveal that HR has important hydrological impact (on evapotranspiration, Bowen ratio, and soil moisture) in ecosystems that have a pronounced dry season but are not overall so dry that sparse vegetation and very low soil moisture limit HR.

scholarly journals The Climate Impact of COVID19 Induced Contrail Changes

2021 ◽  
Author(s):  
Andrew Gettelman ◽  
Chieh-Chieh Chen ◽  
Charles G. Bardeen
Keyword(s):  
Surface Temperature ◽  
Land Surface ◽  
Radiative Forcing ◽  
Climate Impact ◽  
Climate Impacts ◽  
Infrared Heating ◽  
Boreal Summer ◽  
Surface Temperature Change ◽  
Effective Radiative Forcing ◽  
Standard Deviations

Abstract. The COVID19 pandemic caused significant economic disruption in 2020 and severely impacted air traffic. We use a state of the art Earth System Model and ensembles of tightly constrained simulations to evaluate the effect of the reductions in aviation traffic on contrail radiative forcing and climate in 2020. In the absence of any COVID19 pandemic caused reductions, the model simulates a contrail Effective Radiative Forcing (ERF) 62 ± 59 m Wm−2 (2 standard deviations). The contrail ERF has complex spatial and seasonal patterns that combine the offsetting effect of shortwave (solar) cooling and longwave (infrared) heating from contrails and contrail cirrus. Cooling is larger in June–August due to the preponderance of aviation in the N. Hemisphere, while warming occurs throughout the year. The spatial and seasonal forcing variations also map onto surface temperature variations. The net land surface temperature change due to contrails in a normal year is estimated at 0.13 ± 0.04 K (2 standard deviations) with some regions warming as much as 0.7 K. The effect of COVID19 reductions in flight traffic decreased contrails. The unique timing of such reductions, which were maximum in N. Hemisphere spring and summer when the largest contrail cooling occurs, means that cooling due to fewer contrails in boreal spring and fall was offset by warming due to fewer contrails in boreal summer to give no significant annual averaged ERF from contrail changes in 2020. Despite no net significant global ERF, because of the spatial and seasonal timing of contrail ERF, some land regions that would have cooled slightly (minimum −0.2 K) but significantly from contrail changes in 2020. The implications for future climate impacts of contrails are discussed.

scholarly journals A comprehensive approach to analyze discrepancies between land surface models and in-situ measurements: a case study over the US and Illinois with SECHIBA forced by NLDAS

2012 ◽  
Vol 16 (11) ◽  
pp. 3973-3988 ◽  
Author(s):  
M. Guimberteau ◽  
A. Perrier ◽  
K. Laval ◽  
J. Polcher
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Field Capacity ◽  
Land Surface Models ◽  
Data Set ◽  
Wetness Index ◽  
Surface Models ◽  
The Us ◽  
Vegetation Parameters

Abstract. The purpose of this study is to test the ability of the Land Surface Model SECHIBA to simulate water budget and particularly soil moisture at two different scales: regional and local. The model is forced by NLDAS data set at 1/8th degree resolution over the 1997–1999 period. SECHIBA gives satisfying results in terms of evapotranspiration and runoff over the US compared with four other land surface models, all forced by NLDAS data set for a common time period. The simulated soil moisture is compared to in-situ data from the Global Soil Moisture Database across Illinois by computing a soil wetness index. A comprehensive approach is performed to test the ability of SECHIBA to simulate soil moisture with a gradual change of the vegetation parameters closely related to the experimental conditions. With default values of vegetation parameters, the model overestimates soil moisture, particularly during summer. Sensitivity tests of the model to the change of vegetation parameters show that the roots extraction parameter has the largest impact on soil moisture, other parameters such as LAI, height or soil resistance having a minor impact. Moreover, a new evapotranspiration computation including bare soil evaporation under vegetation has been introduced into the model. The results point out an improvement of the soil moisture simulation when this effect is taken into account. Finally, soil moisture sensitivity to precipitation variation is addressed and it is shown that soil moisture observations can be rather different, depending on the method of measuring field capacity. When the observed field capacity is deducted from the observed volumetric water profiles, simulated soil wetness index is closer to the observations.

scholarly journals Reconstruction of droughts in India using multiple land-surface models (1951–2015)

2018 ◽  
Vol 22 (4) ◽  
pp. 2269-2284 ◽  
Author(s):  
Vimal Mishra ◽  
Reepal Shah ◽  
Syed Azhar ◽  
Harsh Shah ◽  
Parth Modi ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Crop Yields ◽  
Soil Depth ◽  
Monsoon Season ◽  
Agricultural Drought ◽  
Drought Severity ◽  
Rabi Season ◽  
Community Land Model ◽  
Spatial Coverage

Abstract. India has witnessed some of the most severe historical droughts in the current decade, and severity, frequency, and areal extent of droughts have been increasing. As a large part of the population of India is dependent on agriculture, soil moisture drought affecting agricultural activities (crop yields) has significant impacts on socio-economic conditions. Due to limited observations, soil moisture is generally simulated using land-surface hydrological models (LSMs); however, these LSM outputs have uncertainty due to many factors, including errors in forcing data and model parameterization. Here we reconstruct agricultural drought events over India during the period of 1951–2015 based on simulated soil moisture from three LSMs, the Variable Infiltration Capacity (VIC), the Noah, and the Community Land Model (CLM). Based on simulations from the three LSMs, we find that major drought events occurred in 1987, 2002, and 2015 during the monsoon season (June through September). During the Rabi season (November through February), major soil moisture droughts occurred in 1966, 1973, 2001, and 2003. Soil moisture droughts estimated from the three LSMs are comparable in terms of their spatial coverage; however, differences are found in drought severity. Moreover, we find a higher uncertainty in simulated drought characteristics over a large part of India during the major crop-growing season (Rabi season, November to February: NDJF) compared to those of the monsoon season (June to September: JJAS). Furthermore, uncertainty in drought estimates is higher for severe and localized droughts. Higher uncertainty in the soil moisture droughts is largely due to the difference in model parameterizations (especially soil depth), resulting in different persistence of soil moisture simulated by the three LSMs. Our study highlights the importance of accounting for the LSMs' uncertainty and consideration of the multi-model ensemble system for the real-time monitoring and prediction of drought over India.

scholarly journals A new space-borne perspective of crop productivity variations over the US Corn Belt

2020 ◽  
Author(s):  
Peter Somkuti ◽  
Hartmut Boesch ◽  
Robert Parker ◽  
Alex Webb ◽  
Liang Feng ◽  
...  
Keyword(s):  
Time Series ◽  
Land Surface ◽  
Crop Yields ◽  
Pearson Correlation ◽  
Crop Productivity ◽  
Annual Reports ◽  
Corn Belt ◽  
Ir Radiation ◽  
The Us ◽  
New Space

<p>We analyse inter-annual variations of SIF over the US Corn Belt using a seven-year time series (2010–2016) retrieved from measurements of short-wave IR radiation collected by the Japanese Greenhouse gases Observing SATellite (GOSAT). Using survey data and annual reports from the US Department of Agriculture (USDA) National Agricultural Statistics Service (NASS), we relate anomalies in the GOSAT SIF time series to meteorological and climatic events that affected planting or growing seasons. The events described in the USDA annual reports are confirmed using remote sensing-based data such as land surface temperature, precipitation, water storage anomalies and soil moisture. These datasets were carefully collocated with the GOSAT footprints on a sub-pixel basis to remove any effect that could occur due to different sampling. We find that cumulative SIF, integrated from April to June, tracks the planting progress established in the first half of the planting season (Pearson correlation r > 0.89). Similarly, we show that crop yields for corn (maize) and soybeans are equally well correlated to the integrated SIF from July to October (r > 0.86). Our results for SIF are consistent with reflectance-based vegetation indices, that have a longer established history of crop monitoring. Despite GOSAT’s sparse sampling, we were able to show the potential for using satellite-based SIF to study agriculturally-managed vegetation.</p><p>[1] Somkuti et al., "A new space-borne perspective of crop productivity variations over the US Corn Belt." Agricultural and Forest Meteorology 281 (2020): 107826.</p><p> </p>

Where are the peatlands? Comparing two global peatland maps through L-band brightness temperature simulations

2021 ◽  
Author(s):  
Michel Bechtold ◽  
Sarith P. Mahanama ◽  
Rolf H. Reichle ◽  
Randal D. Koster ◽  
Gabrielle J. M. De Lannoy
Keyword(s):  
Soil Moisture ◽  
Brightness Temperature ◽  
Land Surface ◽  
Land Surface Model ◽  
Open Loop ◽  
Surface Model ◽  
Earth System ◽  
System Models ◽  
L Band ◽  
Earth System Models

<p>Mapping the global peatland distribution is important for embedding peatland processes into Earth System Models. Peatland maps are typically compiled from nation-specific soil or ecosystem maps or based on machine learning tools trained on such data. Here, we evaluate the performance of a land surface model with two different peatland map inputs in providing critical land surface estimates (soil moisture, temperature) to a Radiative Transfer Model (RTM) for L-band brightness temperature (Tb). We hypothesize that an improved performance of the land surface model in Tb space indicates a better spatial peatland distribution input within the footprint of Tb observations (~40 km).</p><p>We employ the NASA Catchment Land Surface Model (CLSM) with a recently added module for peatland hydrology (PEATCLSM modules). We run this model at a 9-km EASEv2 resolution over the Northern Hemisphere for two soil maps that differ in their peatland distributions. The applied soil distributions are: (MAP1) a combination of the Harmonized World Soil Database and the State Soil Geographic Database, also used to generate the Soil Moisture Active Passive (SMAP) Level-4 soil moisture product, and (MAP2) a hybrid of HWSD-STATSGO and the ‘PEATMAP’ product, which is mainly compiled from national peatland maps. MAP2 indicates ~30 % more peatland area over the Northern Hemisphere. For both peat distributions, CLSM is run and parameters of the RTM are calibrated with 10 years of multi-angular L-band Tb observations from the Soil Moisture and Ocean Salinity SMOS mission. Afterwards, CLSM is run together with the calibrated RTM within a data assimilation system, with and without (open-loop) assimilating SMAP Tb observations, for the period 2015-2020. Our results demonstrate that Tb misfits (in both the open-loop and assimilation runs) are reduced in the areas with the largest differences in peat distribution, thus indicating a basic validity of assuming a peatland-like hydrological dynamics for the larger peat extent of MAP2. Results will be discussed in the context of how peatlands are defined in global peatland maps and the question of what is typically modeled as a peatland in Earth System Models. We propose the evaluation of future releases of peatland maps in Tb space as a tool to evaluate their suitability for implementation into Earth System Models.</p>

scholarly journals Large-scale biospheric drought response intensifies linearly with drought duration in arid regions

2020 ◽  
Vol 17 (9) ◽  
pp. 2647-2656 ◽  
Author(s):  
René Orth ◽  
Georgia Destouni ◽  
Martin Jung ◽  
Markus Reichstein
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Arid Regions ◽  
Large Scale ◽  
Crop Yields ◽  
Terrestrial Ecosystems ◽  
Drought Response ◽  
Drought Duration ◽  
Vegetation Models ◽  
Model Benchmark

Abstract. Soil moisture droughts have comprehensive implications for terrestrial ecosystems. Here we study time-accumulated impacts of the strongest observed droughts on vegetation. The results show that drought duration, the time during which surface soil moisture is below seasonal average, is a key diagnostic variable for predicting drought-integrated changes in (i) gross primary productivity, (ii) evapotranspiration, (iii) vegetation greenness, and (iv) crop yields. Drought-integrated anomalies in these vegetation-related variables scale linearly with drought duration with a slope depending on climate. In arid regions, the slope is steep such that vegetation drought response intensifies with drought duration, whereas in humid regions, it is small such that drought impacts on vegetation are weak even for long droughts. These emergent large-scale linearities are not well captured by state-of-the-art hydrological, land surface, and vegetation models. Overall, the linear relationship of drought duration versus vegetation response and crop yield reductions can serve as a model benchmark and support drought impact interpretation and prediction.

scholarly journals Large-scale biospheric drought response intensifies linearly with drought duration

2019 ◽  
Author(s):  
René Orth ◽  
Georgia Destouni ◽  
Martin Jung ◽  
Markus Reichstein
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Large Scale ◽  
Crop Yields ◽  
Terrestrial Ecosystems ◽  
Drought Response ◽  
Drought Duration ◽  
Vegetation Models ◽  
Model Benchmark ◽  
Relationship Of

Abstract. Soil moisture droughts have comprehensive implications for terrestrial ecosystems. Here we study accumulated impacts of the strongest observed droughts on vegetation. The results show that drought duration, the time during which surface soil moisture is below seasonal average, is a key diagnostic variable for predicting drought-integrated changes in (i) gross primary productivity, (ii) evapotranspiration, (iii) vegetation greenness, and (iv) crop yields. Drought-integrated anomalies in these vegetation-related variables scale linearly with drought duration with a slope depending on climate. In arid regions, the slope is steep such that vegetation drought response intensifies with drought duration, whereas in humid regions, it is small such that drought impacts on vegetation are weak even for long droughts. These emergent large-scale linearities are not well captured by state- of-the-art hydrological, land surface and vegetation models. Overall, the linear relationship of drought duration versus vegetation response and crop yield reductions can serve as model benchmark, and support drought impact interpretation and prediction.

Exploring the effects of the land surface on tropical precipitation

2021 ◽  
Author(s):  
Marianne Pietschnig ◽  
Abigail L. S. Swann ◽  
Ruth Geen ◽  
F. Hugo Lambert ◽  
Geoffrey K. Vallis
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Amazon Basin ◽  
Earth System ◽  
Maritime Continent ◽  
Surface Hydrology ◽  
System Models ◽  
Tropical Precipitation ◽  
Land Surface Hydrology ◽  
Earth System Models

<p>Projected precipitation changes over tropical land tend to be enhanced by vegetation responses to CO<sub>2</sub> forcing in Earth System Models. Projected decreases in rainfall over the Amazon basin and increases over the Maritime Continent are both stronger when plant physiological changes are modelled than if these changes are neglected, but the reasons for this amplification remain unclear. The responses of vegetation to increasing CO<sub>2 </sub>levels are complex and uncertain, but changes in stomatal conductance likely dominate the evapotranspiration response in Earth System Models.</p><p>We investigate why vegetation changes cause precipitation to increase more strongly over the Maritime Continent while decreasing more strongly over the Amazon basin. We employ an idealized Atmospheric General Circulation Model with a simplified vegetation scheme that captures CO<sub>2</sub>-driven stomatal closure.</p><p>We find that – counter-intuitively – rainfall is enhanced over a narrow rectangular island when terrestrial evaporation falls to zero with high CO<sub>2</sub>. Strong heating and ascent over the island trigger moisture advection from the surrounding ocean. In contrast, over larger continents rainfall depends on continental moisture recycling.</p><p>Simulations with two large rectangular continents representing South America and Africa reveal that the stronger decrease in rainfall over the Amazon basin is due to a combination of local and remote effects:</p><p>Finally, we investigate the impact of land-surface hydrology on continental rainfall on seasonal timescales. Using our idealized model and realistic continents, we study the strength of the South East Asian monsoon for different continental evaporation schemes. Surprisingly, when terrestrial evapotranspiration is unlimited (i.e. does not depend on soil moisture availability), monsoon precipitation is much weaker than when terrestrial evapotranspiration is limited by soil moisture. In order to explain this behavior, we compare the atmospheric energy budgets and circulation between the simulations.</p><p>Our results show that the land-surface hydrology plays an important role in modifying tropical precipitation and atmospheric dynamics on seasonal timescales and in the long-term under climate change, and that further investigation into the topic is called for.</p>

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