27 Search Results

Leaf area index (LAI) is increasing throughout the globe, implying Earth greening. Global modeling studies support this contention, yet satellite observations and model simulations have never been directly compared. Here, for the first time, a coupled land–climate model was used to quantify the potential impact of the satellite-observed Earth greening over the past 30 years on the terrestrial water cycle. The global LAI enhancement of 8% between the early 1980s and the early 2010s is modeled to have caused increases of 12.0 ± 2.4 mm yr ⁻¹ in evapotranspiration and 12.1 ± 2.7 mm yr ⁻¹ in precipitation—about 55% ± 25% and 28% ± 6% of the observed increases in land evapotranspiration and precipitation, respectively. In wet regions, the greening did not significantly decrease runoff and soil moisture because it intensified moisture recycling through a coincident increase of evapotranspiration and precipitation. But in dry regions, including the Sahel, west Asia, northern India, the western United States, and the Mediterranean coast, the greening was modeled to significantly decrease soil moisture through its coupling with the atmospheric water cycle. This modeled soil moisture response, however, might have biases resulting from the precipitation biases in the model. For example, the model dry bias might have underestimated the soil moisture response in the observed dry area (e.g., the Sahel and northern India) given that the modeled soil moisture is near the wilting point. Thus, an accurate representation of precipitation and its feedbacks in Earth system models is essential for simulations and predictions of how soil moisture responds to LAI changes, and therefore how the terrestrial water cycle responds to climate change.

Generalized equilibrium feedback assessment (GEFA) is a potentially valuable multivariate statistical tool for extracting vegetation feedbacks to the atmosphere in either observations or coupled Earth system models. The reliability of GEFA at capturing the terrestrial impacts on regional climate is demonstrated in this paper using the National Center for Atmospheric Research Community Earth System Model (CESM), with focus on North Africa. The feedback is assessed statistically by applying GEFA to output from a fully coupled control run. To reduce the sampling error caused by short data records, the traditional or full GEFA is refined through stepwise GEFA by dropping unimportant forcings. Two ensembles of dynamical experiments are developed for the Sahel or West African monsoon region against which GEFA-based vegetation feedbacks are evaluated. In these dynamical experiments, regional leaf area index (LAI) is modified either alone or in conjunction with soil moisture, with the latter runs motivated by strong regional soil moisture–LAI coupling. Stepwise GEFA boasts higher consistency between statistically and dynamically assessed atmospheric responses to land surface anomalies than full GEFA, especially with short data records. GEFA-based atmospheric responses are more consistent with the coupled soil moisture–LAI experiments, indicating that GEFA is assessing the combined impacts of coupled vegetation and soil moisture. Finally, both the statistical and dynamical assessments reveal a negative vegetation–rainfall feedback in the Sahel associated with an atmospheric stability mechanism in CESM versus a weaker positive feedback in the West African monsoon region associated with a moisture recycling mechanism in CESM.

Land-atmosphere interactions are important to climate prediction, but the underlying effects of surface forcing of the atmosphere are not well understood. In the U.S. Southern Great Plains, grassland/pasture and winter wheat are the dominant land covers but have distinct growing periods that may differently influence land-atmosphere coupling during spring and summer. Variables that influence surface flux partitioning can change seasonally, depending on the state of local vegetation. We use surface observations from multiple sites in the U.S. Department of Energy Atmospheric Radiation Measurement Southern Great Plains Climate Research Facility and statistical modeling at a paired grassland/agricultural site within this facility to quantify land cover influence on surface energy balance and variables controlling evaporative fraction (latent heat flux normalized by the sum of sensible and latent heat fluxes). We demonstrate that the radiative balance and evaporative fraction are closely related to green leaf area at both winter wheat and grassland/pasture sites and that the early summer harvest of winter wheat abruptly shifts the relationship between evaporative fraction and surface state variables. Prior to harvest, evaporative fraction of winter wheat is strongly influenced by leaf area and soil-atmosphere temperature differences. After harvest, variations in soil moisture have a stronger effect on evaporative fraction. Finally, this is in contrast with grassland/pasture sites, where variation in green leaf area has a large influence on evaporative fraction throughout spring and summer, and changes in soil-atmosphere temperature difference and soil moisture are of relatively minor importance.

Methane (CH₄) emissions and carbon uptake in temperate freshwater wetlands act in opposing directions in the context of global radiative forcing. Large uncertainties exist for the rates of CH₄ emissions making it difficult to determine the extent that CH₄ emissions counteract the carbon sequestration of wetlands. Urban temperate wetlands are typically small and feature highly heterogeneous land cover, posing an additional challenge to determining their CH₄ budget. The data analysis approach we introduce here combines two different CH₄ flux measurement techniques to overcome scale and heterogeneity problems and determine the overall CH₄ budget of a small, heterogeneous, urban wetland landscape. Temporally intermittent point measurements from non-steady-state chambers provided information about patch-level heterogeneity of fluxes, while continuous, high temporal resolution flux measurements using the eddy-covariance (EC) technique provided information about the temporal dynamics of the fluxes. Patch-level scaling parameterization was developed from the chamber data to scale eddy covariance data to a ‘fixed-frame’, which corrects for variability in the spatial coverage of the eddy covariance observation footprint at any single point in time. Finally, by combining two measurement techniques at different scales, we addressed shortcomings of both techniques with respect to heterogeneous wetland sites.

Accurate simulation of the South Asian summer monsoon (SAM) is still an unresolved challenge. There has not been a benchmark effort to decipher the origin of undesired yet virtually invariable unsuccessfulness of general circulation models (GCMs) over this region. This study analyzes a large ensemble of CMIP5 GCMs to show that most of the simulation errors in the precipitation distribution and their driving mechanisms are systematic and of similar nature across the GCMs, with biases in meridional differential heating playing a critical role in determining the timing of monsoon onset over land, the magnitude of seasonal precipitation distribution and the trajectories of monsoon depressions. Errors in the pre-monsoon heat low over the lower latitudes and atmospheric latent heating over the slopes of Himalayas and Karakoram Range induce significant errors in the atmospheric circulations and meridional differential heating. Lack of timely precipitation further exacerbates such errors by limiting local moisture recycling and latent heating aloft from convection. Most of the summer monsoon errors and their sources are reproducible in the land–atmosphere configuration of a GCM when it is configured at horizontal grid spacing comparable to the CMIP5 GCMs. While an increase in resolution overcomes many modeling challenges, coarse resolution is not necessarily the primary driver in the exhibition of errors over South Asia. These results highlight the importance of previously less well known pre-monsoon mechanisms that critically influence the strength of SAM in the GCMs and highlight the importance of land–atmosphere interactions in the development and maintenance of SAM.

Accurate simulation of the South Asian summer monsoon (SAM) is still an unresolved challenge. There has not been a benchmark effort to decipher the origin of undesired yet virtually invariable unsuccessfulness of general circulation models (GCMs) over this region. Our study analyzes a large ensemble of CMIP5 GCMs to show that most of the simulation errors in the precipitation distribution and their driving mechanisms are systematic and of similar nature across the GCMs, with biases in meridional differential heating playing a critical role in determining the timing of monsoon onset over land, the magnitude of seasonal precipitation distribution and the trajectories of monsoon depressions. Furthermore, errors in the pre-monsoon heat low over the lower latitudes and atmospheric latent heating over the slopes of Himalayas and Karakoram Range induce significant errors in the atmospheric circulations and meridional differential heating. Lack of timely precipitation further exacerbates such errors by limiting local moisture recycling and latent heating aloft from convection. Most of the summer monsoon errors and their sources are reproducible in the land–atmosphere configuration of a GCM when it is configured at horizontal grid spacing comparable to the CMIP5 GCMs. And while an increase in resolution overcomes many modeling challenges, coarse resolution is not necessarily the primary driver in the exhibition of errors over South Asia. Our results highlight the importance of previously less well known pre-monsoon mechanisms that critically influence the strength of SAM in the GCMs and highlight the importance of land–atmosphere interactions in the development and maintenance of SAM.

Here, conventional global climate models are prone to producing unrealistic land-atmosphere coupling signals. Cumulus and convection parameterizations are natural culprits but the effect of bypassing them with explicitly resolved convection on global land-atmosphere coupling dynamics has not been explored systematically. We apply a suite of modern land-atmosphere coupling diagnostics to isolate the effect of cloud Superparameterization in the Community Atmosphere Model (SPCAM) v3.5, focusing on both the terrestrial segment (i.e., soil moisture and surface turbulent fluxes interaction) and atmospheric segment (i.e., surface turbulent fluxes and precipitation interaction) in the water pathway of the landatmosphere feedback loop. At daily timescales, SPCAM produces stronger uncoupled terrestrial signals (negative sign) over tropical rainforests in wet seasons, reduces the terrestrial coupling strength in the Central Great Plain in American, and reverses the coupling sign (from negative to positive) over India in the boreal summer season—all favorable improvements relative to reanalysis-forced land modeling. Analysis of the triggering feedback strength (TFS) and amplification feedback strength (AFS) shows that SPCAM favorably reproduces the observed geographic patterns of these indices over North America, with the probability of afternoon precipitation enhanced by high evaporative fraction along the eastern United States and Mexico, while conventional CAM does not capture this signal. We introduce a new diagnostic called the Planetary Boundary Layer (PBL) Feedback Strength (PFS), which reveals that SPCAM exhibits a tight connection between the responses of the lifting condensation level, the PBL height, and the rainfall triggering to surface turbulent fluxes; a triggering disconnect is found in CAM.

The diurnal and seasonal water cycles in the Amazon remain poorly simulated in general circulation models, exhibiting peak evapotranspiration in the wrong season and rain too early in the day. We show that those biases are not present in cloud-resolving simulations with parameterized large-scale circulation. The difference is attributed to the representation of the morning fog layer, and to more accurate characterization of convection and its coupling with large-scale circulation. The morning fog layer, present in the wet season but absent in the dry season, dramatically increases cloud albedo, which reduces evapotranspiration through its modulation of the surface energy budget. Finally, these results highlight the importance of the coupling between the energy and hydrological cycles and the key role of cloud albedo feedback for climates over tropical continents.

Climate extremes such as drought and heat waves can cause substantial reductions in terrestrial carbon uptake. Advancing projections of the carbon uptake response to future climate extremes depends on (1) identifying mechanistic links between the carbon cycle and atmospheric drivers, (2) detecting and attributing uptake changes, and (3) evaluating models of land response and atmospheric forcing. Here, we combine model simulations, remote sensing products, and ground observations to investigate the impact of climate variability on carbon uptake in the Texas-northern Mexico region. Specifically, we (1) examine the relationship between drought, carbon uptake, and variability of El Niño–Southern Oscillation (ENSO) and the North Atlantic Oscillation (NAO) using the Joint UK Land-Environment Simulator (JULES) biosphere simulations from 1950–2012, (2) quantify changes in carbon uptake during record drought conditions in 2011, and (3) evaluate JULES carbon uptake and soil moisture in 2011 using observations from remote sensing and a network of flux towers in the region. Long-term simulations reveal systematic decreases in regional-scale carbon uptake during negative phases of ENSO and NAO, including amplified reductions of gross primary production (GPP) (-0.42 ± 0.18 Pg C yr^-1) and net ecosystem production (NEP) (-0.14 ± 0.11 Pg C yr^-1) during strong La Niña years. The 2011 megadrought caused some of the largest declines of GPP (-0.50 Pg C yr^-1) and NEP (-0.23 Pg C yr^-1) in our simulations. In 2011, consistent declines were found in observations, including high correlation of GPP and surface soil moisture (r = 0.82 ± 0.23, p = 0.012) in remote sensing-based products. These results suggest a large-scale response of carbon uptake to ENSO and NAO, and highlight a need to improve model predictions of ENSO and NAO in order to improve predictions of future impacts on the carbon cycle and the associated feedbacks to climate change.

Surface-to-atmosphere emissions of dimethyl sulfide (DMS) may impact global climate 44 through the formation of gaseous sulfuric acid, which can yield secondary sulfate 45 aerosols and contribute to new particle formation. While oceans are generally 46 considered the dominant source of DMS, a shortage of ecosystem observations prevents 47 an accurate analysis of terrestrial DMS sources. Using mass spectrometry, we quantified 48 ambient DMS mixing ratios within and above a primary rainforest ecosystem in the 49 central Amazon Basin in real-time (2010-2011) and at high vertical resolution (2013-50 2014). Elevated but highly variable DMS mixing ratios were observed within the 51 canopy, showing clear evidence of a net ecosystem source to the atmosphere during 52 both day and night in both the dry and wet seasons. Periods of high DMS mixing ratios 53 lasting up to 8 hours (up to 160 ppt) often occurred within the canopy and near the 54 surface during many evenings and nights. Daytime gradients showed mixing ratios (up 55 to 80 ppt) peaking near the top of the canopy as well as near the ground following a rain 56 event. The spatial and temporal distribution of DMS suggests that ambient levels and 57 their potential climatic impacts are dominated by local soil and plant emissions. A soil 58 source was confirmed by measurements of DMS emission fluxes from Amazon soils as 59 a function of temperature and soil moisture. Furthermore, light and temperature 60 dependent DMS emissions were measured from seven tropical tree species. Our study 61 has important implications for understanding terrestrial DMS sources and their role in 62 coupled land-atmosphere climate feedbacks. 63