Vegetation response to extreme climate events on the Mongolian Plateau from 2000 to 2010

Climate change has led to more frequent extreme winters (aka, dzud) and summer droughts on the Mongolian Plateau during the last decade. Among these events, the 2000–2002 combined summer drought–dzud and 2010 dzud were the most severe on vegetation. We examined the vegetation response to these extremes through the past decade across the Mongolian Plateau as compared to decadal means. We first assessed the severity and extent of drought using the Tropical Rainfall Measuring Mission (TRMM) precipitation data and the Palmer drought severity index (PDSI). We then examined the effects of drought by mapping anomalies in vegetation indices (EVI, EVI2) and land surface temperature derived from MODIS and AVHRR for the period of 2000–2010. We found that the standardized anomalies of vegetation indices exhibited positively skewed frequency distributions in dry years, which were more common for the desert biome than for grasslands. For the desert biome, the dry years (2000–2001, 2005 and 2009) were characterized by negative anomalies with peak values between �1.5 and �0.5 and were statistically different (P \u3c 0:001) from relatively wet years (2003, 2004 and 2007). Conversely, the frequency distributions of the dry years were not statistically different (p \u3c 0:001) from those of the relatively wet years for the grassland biome, showing that they were less responsive to drought and more resilient than the desert biome. We found that the desert biome is more vulnerable to drought than the grassland biome. Spatially averaged EVI was strongly correlated with the proportion of land area affected by drought (PDSI \u3c �1) in Inner Mongolia (IM) and Outer Mongolia (OM), showing that droughts substantially reduced vegetation activity. The correlation was stronger for the desert biome (R2 D 65 and 60, p \u3c 0:05) than for the IM grassland biome (R2 D 53, p \u3c 0:05). Our results showed significant differences in the responses to extreme climatic events (summer drought and dzud) between the desert and grassland biomes on the Plateau

Monitoring global vegetation dynamics with coarse and moderate resolution satellite data

Earth's annual average temperature has increased by about 0.6°C during the past three decades. This warming pulse has brought many changes in the climatic system. For example, the Amazon forests of South America experienced frequent droughts possibly from altered air-sea interaction patterns in the Pacific and Atlantic oceans. The response of vegetation to this unprecedented rate of warming is the subject of this dissertation. Vegetation greenness levels, a surrogate of vegetation photosynthetic activity, recorded by satellite-borne sensors offer repetitive synoptic views of the Earth's vegetation. This period of extraordinary warming coincided with the availability of multiple data sets of vegetation greenness levels from different satellites, thus providing an unique opportunity to assess the impact of warming on vegetation. The Amazon region has suffered two severe droughts during this decade - the so-called "once-in-a-century" drought in 2005 and an even stronger drought in 2010. Vegetation browning during the 2010 drought was four times greater than that in 2005 (2.4 million km^2). Notably, 51% of all drought-stricken forests showed browning in 2010 (1.68 million km^2) compared to only 14% in 2005 (0.32 million km^2). This large-scale decline in vegetation greenness denotes significant loss of photosynthetic capacity of Amazonian vegetation and thus a major perturbation to the global carbon cycle. In the northern latitudes (>50°N), vegetation seasonality (SV) is tightly coupled to temperature seasonality (ST). As ST diminished, so did SV. The observed declines of ST and SV are equivalent to 4 and 7° (5 and 6°) latitudinal shifts equatorward during the past 30 years in the Arctic (Boreal) region. Analysis of simulations from 17 state-of-the-art climate models indicates an additional ST diminishment equivalent to a 20° equatorward shift this century. How SV will change in response to such large projected ST declines is not well understood. Hence there is a need for continued monitoring of northern lands as their seasonal temperature profiles evolve to resemble those further south. The results presented in this dissertation provide a better understanding of the impact of recent warming on three pristine ecosystems - the Amazonian forests, and the Arctic and Boreal ecosystems

Satellite remote sensing of vegetation dynamics in the context of climate change

Vegetation is a key component of the Earth's climate system. Understanding vegetation dynamics in a changing climate requires both in situ and remote sensing data. Satellite remote sensing is especially indispensible for continuous monitoring of vegetation over large areas. This dissertation is focused on investigation of vegetation dynamics in the broader context of climate change using satellite data over two critical regions: the arctic-boreal area in the northern high latitudes and Amazonia in South America. The northern high latitudes have experienced amplified warming. We found the response of the arctic-boreal vegetation to this warming to be different between North America and Eurasia during a 30-year period since 1982: the relationship between vegetation green-up and temperature rise was stable over Eurasia, but in North America, the amount of vegetation green-up per unit amount of warming has decreased since the beginning of 21st century. This could partly be explained by the unmatched northward movements of temperature and precipitation patterns in North America. The Amazonian rainforests have highly dense canopies of green leaves. In such dense media, reflection of solar radiation tends to saturate. Thus, the satellite measurements are weakly sensitive to vegetation changes. At the same time, the data are strongly influenced by changing sun-sensor geometry. This makes it difficult to discriminate between vegetation changes and sun-sensor geometry effects. We developed a new physically based approach to detect changes in dense forests. Analyses of several years of data from three sensors on two satellites under a range of sun-sensor geometries provide robust evidence for a sunlight driven seasonal cycle in structure and greenness of Amazonian rainforests. The 2005 and 2010 dry-season droughts decreased the photosynthetic activity of Amazonian rainforests. We demonstrate that satellite data capture such decreases. Furthermore, we show that in 2004 and 2007, when there was lower wet-season water abundance compared to normal years, the photosynthetic activity of Amazonian forests also decreased. Potentially frequent water deficits over Amazon in the future, irrespective of whether they occur in the dry or wet season, will decrease the photosynthetic activity of Amazonian forests, and provide a positive feedback to global warming

Post-drought decline of the Amazon carbon sink

Amazon forests have experienced frequent and severe droughts in the past two decades. However, little is known about the large-scale legacy of droughts on carbon stocks and dynamics of forests. Using systematic sampling of forest structure measured by LiDAR waveforms from 2003 to 2008, here we show a significant loss of carbon over the entire Amazon basin at a rate of 0.3 ± 0.2 (95% CI) PgC yr−1 after the 2005 mega-drought, which continued persistently over the next 3 years (2005–2008). The changes in forest structure, captured by average LiDAR forest height and converted to above ground biomass carbon density, show an average loss of 2.35 ± 1.80 MgC ha−1 a year after (2006) in the epicenter of the drought. With more frequent droughts expected in future, forests of Amazon may lose their role as a robust sink of carbon, leading to a significant positive climate feedback and exacerbating warming trends.The research was partially supported by NASA Terrestrial Ecology grant at the Jet Propulsion Laboratory, California Institute of Technology and partial funding to the UCLA Institute of Environment and Sustainability from previous National Aeronautics and Space Administration and National Science Foundation grants. The authors thank NSIDC, BYU, USGS, and NASA Land Processes Distributed Active Archive Center (LP DAAC) for making their data available. (NASA Terrestrial Ecology grant at the Jet Propulsion Laboratory, California Institute of Technology)Published versio

Interpretation of Variations in Modis-Measured Greenness Levels of Amazon Forests During 2000 to 2009

This work investigates variations in satellite-measured greenness of Amazon forests using ten years of NASA Moderate Resolution Imaging Spectroradiometer (MODIS) enhanced vegetation index (EVI) data. Corruption of optical remote sensing data with clouds and aerosols is prevalent in this region; filtering corrupted data causes spatial sampling constraints, as well as reducing the record length, which introduces large biases in estimates of greenness anomalies. The EVI data, analyzed in multiple ways and taking into account EVI accuracy, consistently show a pattern of negligible changes in the greenness levels of forests both in the area affected by drought in 2005 and outside it. Small random patches of anomalous greening and browning-especially prominent in 2009-appear in all ten years, irrespective of contemporaneous variations in precipitation, but with no persistence over time. The fact that over 90% of the EVI anomalies are insignificantly small-within the envelope of error (95% confidence interval) in EVI-warrants cautious interpretation of these results: there were no changes in the greenness of these forests, or if there were changes, the EVI data failed to capture these either because the constituent reflectances were saturated or the moderate resolution precluded viewing small-scale variations. This suggests a need for more accurate and spatially resolved synoptic views from satellite data and corroborating comprehensive ground sampling to understand the greenness dynamics of these forests

Asynchronous Amazon forest canopy phenology indicates adaptation to both water and light availability

Amazon forests represent nearly half of all tropical vegetation biomass and, through photosynthesis and respiration, annually process more than twice the amount of estimated carbon (CO2) from fossil fuel emissions. Yet the seasonality of Amazon canopy cover, and the extent to which seasonal fluctuations in water availability and photosynthetically available radiation influence these processes, is still poorly understood. Implementing six remotely sensed data sets spanning nine years (2003–2011), with reported field and flux tower data, we show that southern equatorial Amazon forests exhibit a distinctive seasonal signal. Seasonal timing of water availability, canopy biomass growth and net leaf flush are asynchronous in regions with short dry seasons and become more synchronous across a west-to-east longitudinal moisture gradient of increasing dry season. Forest cover is responsive to seasonal disparities in both water and solar radiation availability, temporally adjusting net leaf flush to maximize use of these generally abundant resources, while reducing drought susceptibility. An accurate characterization of this asynchronous behavior allows for improved understanding of canopy phenology across contiguous tropical forests and their sensitivity to climate variability and drought

Understanding Climate-Vegetation Interactions in Global Rainforests Through a GP-Tree Analysis

The tropical rainforests are the largest reserves of terrestrial carbon and, therefore, the future of these rainforests is a question that is of immense importance in the geoscience research community. With the recent severe Amazonian droughts in 2005 and 2010 and on-going drought in the Congo region for more than two decades, there is growing concern that these forests could succumb to precipitation reduction, causing extensive carbon release and feedback to the carbon cycle. However, there is no single ecosystem model that quantifies the relationship between vegetation health in these rainforests and climatic factors. Small scale studies have used statistical correlation measure and simple linear regression to model climate-vegetation interactions, but suffer from the lack of comprehensive data representation as well as simplistic assumptions about dependency of the target on the covariates. In this paper we use genetic programming (GP) based symbolic regression for discovering equations that govern the vegetation climate dynamics in the rainforests. Expecting micro-regions within the rainforests to have unique characteristics compared to the overall general characteristics, we use a modified regression-tree based hierarchical partitioning of the space to build individual models for each partition. The discovery of these equations reveal very interesting characteristics about the Amazon and the Congo rainforests. Our method GP-tree shows that the rainforests exhibit tremendous resiliency in the face of extreme climatic events by adapting to changing conditions

Do the recent severe droughts in the Amazonia have the same period of length?

We propose a new measure based on drought period length to assess the temporal difference between the recent two severe droughts of 2005 and 2010 in the Amazonia. The sensitivity of the measure is demonstrated by disclosing the distinct spatial responding mechanisms of the Northeastern and Southwestern Amazon (NA, SA) to the surrounding sea surface temperature (SST) variabilities. The Pacific and Atlantic oceans have different roles on the precipitation patterns in Amazonia. More specifically, the very dry periods in the NA are influenced by El Ni\~no events, while the very dry periods in the SA are affected by the anomalously warming of the SST in the North Atlantic. We show convincingly that the drought 2005 hit SA, which is caused by the North Atlantic only. There are two phases in the drought 2010: (i) it was started in the NA in August 2009 affected by the El Ni\~no event, and (ii) later shifted the center of action to SA resulted from anomalously high SST in North Atlantic, which further intensifies the impacts on the spatial coverage.Comment: 5 figure

Vegetation anomalies caused by antecedent precipitation in most of the world

Quantifying environmental controls on vegetation is critical to predict the net effect of climate change on global ecosystems and the subsequent feedback on climate. Following a non-linear Granger causality framework based on a random forest predictive model, we exploit the current wealth of multi-decadal satellite data records to uncover the main drivers of monthly vegetation variability at the global scale. Results indicate that water availability is the most dominant factor driving vegetation globally: about 61% of the vegetated surface was primarily water-limited during 1981-2010. This included semiarid climates but also transitional ecoregions. Intraannually, temperature controls Northern Hemisphere deciduous forests during the growing season, while antecedent precipitation largely dominates vegetation dynamics during the senescence period. The uncovered dependency of global vegetation on water availability is substantially larger than previously reported. This is owed to the ability of the framework to (1) disentangle the co-linearities between radiation/temperature and precipitation, and (2) quantify non-linear impacts of climate on vegetation. Our results reveal a prolonged effect of precipitation anomalies in dry regions: due to the long memory of soil moisture and the cumulative, nonlinear, response of vegetation, water-limited regions show sensitivity to the values of precipitation occurring three months earlier. Meanwhile, the impacts of temperature and radiation anomalies are more immediate and dissipate shortly, pointing to a higher resilience of vegetation to these anomalies. Despite being infrequent by definition, hydro-climatic extremes are responsible for up to 10% of the vegetation variability during the 1981-2010 period in certain areas, particularly in water-limited ecosystems. Our approach is a first step towards a quantitative comparison of the resistance and resilience signature of different ecosystems, and can be used to benchmark Earth system models in their representations of past vegetation sensitivity to changes in climate

A comparison of plot-based satellite and Earth system model estimates of tropical forest net primary production

Net primary production (NPP) by plants represents the largest annual flux of carbon dioxide (CO2) from the atmosphere to the terrestrial biosphere, playing a critical role in the global carbon (C) cycle and the Earth’s climate. Rates of NPP in tropical forests are thought to be among the highest on Earth, but debates about the magnitude, patterns, and controls of NPP in the tropics highlight uncertainty in our understanding of how tropical forests may respond to environmental change. Here, we compared tropical NPP estimates generated using three common approaches: (1) field-based methods scaled from plot-level measurements of plant biomass, (2) radiation-based methods that model NPP from satellite-derived radiation absorption by plants, (3) and biogeochemical model-based methods. For undisturbed tropical forests as a whole, the three methods produced similar NPP estimates (i.e. about 10 Pg C yr1). However, the three different approaches produced vastly different patterns of NPP both in space and through time, suggesting that our understanding of tropical NPP is poor and that our ability to predict the response of NPP in the tropics to environmental change is limited. To address this shortcoming, we suggest the development of an expanded, high-density, permanent network of sites where NPP is continuously evaluated using multiple approaches. Well-designed NPP megatransects that include a high-density plot network would significantly increase the accuracy and certainty in the observed rates and patterns of tropical NPP and improve the reliability of Earth system models used to predict NPP–carbon cycle–climate interactions into the futur