Climatic effects of 1950-2050 changes in US anthropogenic aerosols - Part 2: Climate response

We investigate the climate response to changing US anthropogenic aerosol sources over the 1950–2050 period by using the NASA GISS general circulation model (GCM) and comparing to observed US temperature trends. Time-dependent aerosol distributions are generated from the GEOS-Chem chemical transport model applied to historical emission inventories and future projections. Radiative forcing from US anthropogenic aerosols peaked in 1970–1990 and has strongly declined since due to air quality regulations. We find that the regional radiative forcing from US anthropogenic aerosols elicits a strong regional climate response, cooling the central and eastern US by 0.5–1.0 °C on average during 1970–1990, with the strongest effects on maximum daytime temperatures in summer and autumn. Aerosol cooling reflects comparable contributions from direct and indirect (cloud-mediated) radiative effects. Absorbing aerosol (mainly black carbon) has negligible warming effect. Aerosol cooling reduces surface evaporation and thus decreases precipitation along the US east coast, but also increases the southerly flow of moisture from the Gulf of Mexico resulting in increased cloud cover and precipitation in the central US. Observations over the eastern US show a lack of warming in 1960–1980 followed by very rapid warming since, which we reproduce in the GCM and attribute to trends in US anthropogenic aerosol sources. Present US aerosol concentrations are sufficiently low that future air quality improvements are projected to cause little further warming in the US (0.1 °C over 2010–2050). We find that most of the warming from aerosol source controls in the US has already been realized over the 1980–2010 period

Projected Spatiotemporal Dynamics of Drought under Global Warming in Central Asia

Drought, one of the most common natural disasters that have the greatest impact on human social life, has been extremely challenging to accurately assess and predict. With global warming, it has become more important to make accurate drought predictions and assessments. In this study, based on climate model data provided by the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP), we used the Palmer Drought Severity Index (PDSI) to analyze and project drought characteristics and their trends under two global warming scenarios—1.5 °C and 2.0 °C—in Central Asia. The results showed a marked decline in the PDSI in Central Asia under the influence of global warming, indicating that the drought situation in Central Asia would further worsen under both warming scenarios. Under the 1.5 °C warming scenario, the PDSI in Central Asia decreased first and then increased, and the change time was around 2080, while the PDSI values showed a continuous decline after 2025 in the 2.0 °C warming scenario. Under the two warming scenarios, the spatial characteristics of dry and wet areas in Central Asia are projected to change significantly in the future. In the 1.5 °C warming scenario, the frequency of drought and the proportion of arid areas in Central Asia were significantly higher than those under the 2.0 °C warming scenario. Using the Thornthwaite (TH) formula to calculate the PDSI produced an overestimation of drought, and the Penman–Monteith (PM) formula is therefore recommended to calculate the index

Processes shaping the spatial pattern and seasonality of the surface air temperature response to anthropogenic forcing

In the period 1960-2010, the land surface air temperature (SAT) warmed more rapidly over some regions relative to the global mean. Using a set of time-slice experiments, we highlight how different physical processes shape the regional pattern of SAT warming. The results indicate an essential role of anthropogenic forcing in regional SAT changes from the 1970s to 2000s, and show that both surface-atmosphere interactions and large-scale atmospheric circulation changes can shape regional responses to forcing. Single forcing experiments show that an increase in greenhouse gases (GHG) can lead to regional changes in land surface warming in winter (DJF) due to snow-albedo feedbacks, and in summer (JJA) due to soil-moisture and cloud feedbacks. Changes in anthropogenic aerosol and precursor (AA) emissions induce large spatial variations in SAT, characterized by warming over western Europe, Eurasia, and Alaska. In western Europe, SAT warming is stronger in JJA than in DJF due to substantial increases in clear sky shortwave radiation over Europe, associated with decreases in local AA emissions since the 1980s. In Alaska, the amplified SAT warming in DJF is due to increased downward longwave radiation, which is related to increased water vapor and cloud cover. In this case, although the model was able to capture the regional pattern of SAT change, and the associated local processes, it did not simulate all processes and anomalies correctly. For the Alaskan warming, the model is seen to achieve the correct regional response in the context of a wider North Pacific anomaly that is not consistent with observations. This demonstrates the importance of model evaluation that goes beyond the target variable in detection and attribution studies

Upscaling key ecosystem functions across the conterminous United States by a water-centric ecosystem model

We developed a water-centric monthly scale simulation model (WaSSI-C) by integrating empirical water and carbon flux measurements from the FLUXNET network and an existing water supply and demand accounting model (WaSSI). The WaSSI-C model was evaluated with basin-scale evapotranspiration (ET), gross ecosystem productivity (GEP), and net ecosystem exchange (NEE) estimates by multiple independent methods across 2103 eight-digit Hydrologic Unit Code watersheds in the conterminous United States from 2001 to 2006. Our results indicate that WaSSI-C captured the spatial and temporal variability and the effects of large droughts on key ecosystem fluxes. Our modeled mean (±standard deviation in space) ET (556 ± 228 mm yr−1) compared well to Moderate Resolution Imaging Spectroradiometer (MODIS) based (527 ± 251 mm yr−1) and watershed water balance based ET (571 ± 242 mm yr−1). Our mean annual GEP estimates (1362 ± 688 g C m−2 yr−1) compared well (R2 = 0.83) to estimates (1194 ± 649 g C m−2 yr−1) by eddy flux-based EC-MOD model, but both methods led significantly higher (25–30%) values than the standard MODIS product (904 ± 467 g C m−2 yr−1). Among the 18 water resource regions, the southeast ranked the highest in terms of its water yield and carbon sequestration capacity. When all ecosystems were considered, the mean NEE (−353 ± 298 g C m−2 yr−1) predicted by this study was 60% higher than EC-MOD\u27s estimate (−220 ± 225 g C m−2 yr−1) in absolute magnitude, suggesting overall high uncertainty in quantifying NEE at a large scale. Our water-centric model offers a new tool for examining the trade-offs between regional water and carbon resources under a changing environment

Does metal pollution matter with C retention by rice soil?

Date of Acceptance: 17/07/2015 The research work was supported by the China Natural Science Foundation under a grant number of 40830528 and of 40671180. P.S. is a Royal Scoiety-Wolfson Research Merit Award holder and was supported by additional travel funds from a UK BBSRC China Partnership Award. P.S.’s contribution was supported by the UK-China Sustainable Agriculture Innovation Network (SAIN). D.C. was supported by an additional travel and collaboration funding from the China Ministry of Education under a “111” project.Peer reviewedPublisher PD

Estimates of global terrestrial isoprene emissions using MEGAN (Model of Emissions of Gases and Aerosols from Nature)

Reactive gases and aerosols are produced by terrestrial ecosystems, processed within plant canopies, and can then be emitted into the above-canopy atmosphere. Estimates of the above-canopy fluxes are needed for quantitative earth system studies and assessments of past, present and future air quality and climate. The Model of Emissions of Gases and Aerosols from Nature (MEGAN) is described and used to quantify net terrestrial biosphere emission of isoprene into the atmosphere. MEGAN is designed for both global and regional emission modeling and has global coverage with ~1 km² spatial resolution. Field and laboratory investigations of the processes controlling isoprene emission are described and data available for model development and evaluation are summarized. The factors controlling isoprene emissions include biological, physical and chemical driving variables. MEGAN driving variables are derived from models and satellite and ground observations. Tropical broadleaf trees contribute almost half of the estimated global annual isoprene emission due to their relatively high emission factors and because they are often exposed to conditions that are conducive for isoprene emission. The remaining flux is primarily from shrubs which have a widespread distribution. The annual global isoprene emission estimated with MEGAN ranges from about 500 to 750 Tg isoprene (440 to 660 Tg carbon) depending on the driving variables which include temperature, solar radiation, Leaf Area Index, and plant functional type. The global annual isoprene emission estimated using the standard driving variables is ~600 Tg isoprene. Differences in driving variables result in emission estimates that differ by more than a factor of three for specific times and locations. It is difficult to evaluate isoprene emission estimates using the concentration distributions simulated using chemistry and transport models, due to the substantial uncertainties in other model components, but at least some global models produce reasonable results when using isoprene emission distributions similar to MEGAN estimates. In addition, comparison with isoprene emissions estimated from satellite formaldehyde observations indicates reasonable agreement. The sensitivity of isoprene emissions to earth system changes (e.g., climate and land-use) demonstrates the potential for large future changes in emissions. Using temperature distributions simulated by global climate models for year 2100, MEGAN estimates that isoprene emissions increase by more than a factor of two. This is considerably greater than previous estimates and additional observations are needed to evaluate and improve the methods used to predict future isoprene emissions

A non-linear Granger-causality framework to investigate climate-vegetation dynamics

Satellite Earth observation has led to the creation of global climate data records of many important environmental and climatic variables. These come in the form of multivariate time series with different spatial and temporal resolutions. Data of this kind provide new means to further unravel the influence of climate on vegetation dynamics. However, as advocated in this article, commonly used statistical methods are often too simplistic to represent complex climate-vegetation relationships due to linearity assumptions. Therefore, as an extension of linear Granger-causality analysis, we present a novel non-linear framework consisting of several components, such as data collection from various databases, time series decomposition techniques, feature construction methods, and predictive modelling by means of random forests. Experimental results on global data sets indicate that, with this framework, it is possible to detect non-linear patterns that are much less visible with traditional Granger-causality methods. In addition, we discuss extensive experimental results that highlight the importance of considering non-linear aspects of climate-vegetation dynamics

A meta-analysis of long-term effects of conservation agriculture on maize grain yield under rain-fed conditions

Conservation agriculture involves reduced tillage, permanent soil cover and crop rotations to enhance soil fertility and to supply food from a dwindling land resource. Recently, conservation agriculture has been promoted in Southern Africa, mainly for maize-based farming systems. However, maize yields under rain-fed conditions are often variable. There is therefore a need to identify factors that influence crop yield under conservation agriculture and rain-fed conditions. Here, we studied maize grain yield data from experiments lasting 5 years and more under rain-fed conditions. We assessed the effect of long-term tillage and residue retention on maize grain yield under contrasting soil textures, nitrogen input and climate. Yield variability was measured by stability analysis. Our results show an increase in maize yield over time with conservation agriculture practices that include rotation and high input use in low rainfall areas. But we observed no difference in system stability under those conditions. We observed a strong relationship between maize grain yield and annual rainfall. Our meta-analysis gave the following findings: (1) 92% of the data show that mulch cover in high rainfall areas leads to lower yields due to waterlogging; (2) 85% of data show that soil texture is important in the temporal development of conservation agriculture effects, improved yields are likely on well-drained soils; (3) 73% of the data show that conservation agriculture practices require high inputs especially N for improved yield; (4) 63% of data show that increased yields are obtained with rotation but calculations often do not include the variations in rainfall within and between seasons; (5) 56% of the data show that reduced tillage with no mulch cover leads to lower yields in semi-arid areas; and (6) when adequate fertiliser is available, rainfall is the most important determinant of yield in southern Africa. It is clear from our results that conservation agriculture needs to be targeted and adapted to specific biophysical conditions for improved impact