scholarly journals Empirically Derived Sensitivity of Vegetation to Climate across Global Gradients of Temperature and Precipitation

2017 ◽  
Vol 30 (15) ◽  
pp. 5835-5849 ◽  
Author(s):  
Gregory R. Quetin ◽  
Abigail L. S. Swann
Keyword(s):  
Water Demand ◽  
Ecosystem Functioning ◽  
Spatial Scales ◽  
Terrestrial Ecosystems ◽  
Global Scale ◽  
Atmospheric Water ◽  
Temperature And Precipitation ◽  
Two Factors ◽  
Climate Interactions ◽  
Climate Space

The natural composition of terrestrial ecosystems can be shaped by climate to take advantage of local environmental conditions. Ecosystem functioning (e.g., interaction between photosynthesis and temperature) can also acclimate to different climatological states. The combination of these two factors thus determines ecological–climate interactions. A global empirical map of the sensitivity of vegetation to climate is derived using the response of satellite-observed greenness to interannual variations in temperature and precipitation. Mechanisms constraining ecosystem functioning are inferred by analyzing how the sensitivity of vegetation to climate varies across climate space. Analysis yields empirical evidence for multiple physical and biological mediators of the sensitivity of vegetation to climate at large spatial scales. In hot and wet locations, vegetation is greener in warmer years despite temperatures likely exceeding thermally optimum conditions. However, sunlight generally increases during warmer years, suggesting that the increased stress from higher atmospheric water demand is offset by higher rates of photosynthesis. The sensitivity of vegetation transitions in sign (greener when warmer or drier to greener when cooler or wetter) along an emergent line in climate space with a slope of about 59 mm yr−1 °C−1, twice as steep as contours of aridity. The mismatch between these slopes is evidence at a global scale of the limitation of both water supply due to inefficiencies in plant access to rainfall and plant physiological responses to atmospheric water demand. This empirical pattern can provide a functional constraint for process-based models, helping to improve predictions of the global-scale response of vegetation to a changing climate.

scholarly journals On what scales can GOSAT flux inversions constrain anomalies in terrestrial ecosystems?

2019 ◽  
Author(s):  
Brendan Byrne ◽  
Dylan B. A. Jones ◽  
Kimberly Strong ◽  
Saroja M. Polavarapu ◽  
Anna B. Harper ◽  
...  
Keyword(s):  
Spatial Scales ◽  
Net Ecosystem Exchange ◽  
Terrestrial Ecosystems ◽  
Atmospheric Co2 ◽  
Strong Correlations ◽  
Climate Anomalies ◽  
Temperature And Precipitation ◽  
The Tropics ◽  
Emergent Constraint ◽  
The Impact

Abstract. Interannual variations in temperature and precipitation impact the carbon balance of terrestrial ecosystems, leaving an imprint in atmospheric CO2. Quantifying the impact of climate anomalies on the net ecosystem exchange (NEE) of terrestrial ecosystems can provide a constraint to evaluate terrestrial biosphere models against, and may provide an emergent constraint on the response of terrestrial ecosystems to climate change. We investigate the spatial scales over which interannual variability in NEE can be constrained using atmospheric CO2 observations from the Greenhouse Gases Observing Satellite (GOSAT). NEE anomalies are calculated by performing a series of inversion analyses using the GEOS-Chem model to assimilate GOSAT observations. Monthly NEE anomalies are compared to proxies, variables which are associated with anomalies in the terrestrial carbon cycle, and to upscaled NEE estimates from FLUXCOM. Strong agreement is found in the timing of anomalies in the GOSAT flux inversions with soil temperature and FLUXCOM. Strong correlations are obtained (P  RNINO3.4) in the tropics on continental and larger scales, and in the northern extratropics on sub-continental scales during the summer (R2 ≥ 0.49). These results, in addition to a series of observing system simulation experiments that were conducted, provide evidence that GOSAT flux inversions can isolate anomalies in NEE on continental and larger scales. However, in both the tropics and northern extratropics, the agreement between the inversions and the proxies/FLUXCOM is sensitive to the flux inversion configuration. Our results suggest that regional scales are likely the minimum scales that can be resolved in the tropics using GOSAT observations, but obtaining robust NEE anomaly estimates on these scales may be difficult.

scholarly journals On what scales can GOSAT flux inversions constrain anomalies in terrestrial ecosystems?

2019 ◽  
Vol 19 (20) ◽  
pp. 13017-13035 ◽  
Author(s):  
Brendan Byrne ◽  
Dylan B. A. Jones ◽  
Kimberly Strong ◽  
Saroja M. Polavarapu ◽  
Anna B. Harper ◽  
...  
Keyword(s):  
Interannual Variability ◽  
Spatial Scales ◽  
Net Ecosystem Exchange ◽  
Terrestrial Ecosystems ◽  
Atmospheric Co2 ◽  
Terrestrial Biosphere ◽  
Climate Anomalies ◽  
Temperature And Precipitation ◽  
The Tropics ◽  
The Impact

Abstract. Interannual variations in temperature and precipitation impact the carbon balance of terrestrial ecosystems, leaving an imprint in atmospheric CO2. Quantifying the impact of climate anomalies on the net ecosystem exchange (NEE) of terrestrial ecosystems can provide a constraint to evaluate terrestrial biosphere models against and may provide an emergent constraint on the response of terrestrial ecosystems to climate change. We investigate the spatial scales over which interannual variability in NEE can be constrained using atmospheric CO2 observations from the Greenhouse Gases Observing Satellite (GOSAT). NEE anomalies are calculated by performing a series of inversion analyses using the GEOS-Chem adjoint model to assimilate GOSAT observations. Monthly NEE anomalies are compared to “proxies”, variables that are associated with anomalies in the terrestrial carbon cycle, and to upscaled NEE estimates from FLUXCOM. Statistically significant correlations (P<0.05) are obtained between posterior NEE anomalies and anomalies in soil temperature and FLUXCOM NEE on continental and larger scales in the tropics, as well as in the northern extratropics on subcontinental scales during the summer (R2≥0.49), suggesting that GOSAT measurements provide a constraint on NEE interannual variability (IAV) on these spatial scales. Furthermore, we show that GOSAT flux inversions are generally better correlated with the environmental proxies and FLUXCOM NEE than NEE anomalies produced by a set of terrestrial biosphere models (TBMs), suggesting that GOSAT flux inversions could be used to evaluate TBM NEE fluxes.

scholarly journals 8-Day and Daily Maximum and Minimum Air Temperature Estimation via Machine Learning Method on a Climate Zone to Global Scale

2021 ◽  
Vol 13 (12) ◽  
pp. 2355
Author(s):  
Linglin Zeng ◽  
Yuchao Hu ◽  
Rui Wang ◽  
Xiang Zhang ◽  
Guozhang Peng ◽  
...  
Keyword(s):  
Spatial Scale ◽  
Air Temperature ◽  
Spatial Scales ◽  
Model Performance ◽  
Global Scale ◽  
Scale Model ◽  
Validation Dataset ◽  
Daily Maximum ◽  
Climate Zone ◽  
Temporal And Spatial

Air temperature (Ta) is a required input in a wide range of applications, e.g., agriculture. Land Surface Temperature (LST) products from Moderate Resolution Imaging Spectroradiometer (MODIS) are widely used to estimate Ta. Previous studies of these products in Ta estimation, however, were generally applied in small areas and with a small number of meteorological stations. This study designed both temporal and spatial experiments to estimate 8-day and daily maximum and minimum Ta (Tmax and Tmin) on three spatial scales: climate zone, continental and global scales from 2009 to 2018, using the Random Forest (RF) method based on MODIS LST products and other auxiliary data. Factors contributing to the relation between LST and Ta were determined based on physical models and equations. Temporal and spatial experiments were defined by the rules of dividing the training and validation datasets for the RF method, in which the stations selected in the training dataset were all included or not in the validation dataset. The RF model was first trained and validated on each spatial scale, respectively. On a global scale, model accuracy with a determination coefficient (R2) > 0.96 and root mean square error (RMSE) < 1.96 °C and R2 > 0.95 and RMSE < 2.55 °C was achieved for 8-day and daily Ta estimations, respectively, in both temporal and spatial experiments. Then the model was trained and cross-validated on each spatial scale. The results showed that the data size and station distribution of the study area were the main factors influencing the model performance at different spatial scales. Finally, the spatial patterns of the model performance and variable importance were analyzed. Both daytime and nighttime LST had a significant contribution in the 8-day Tmax estimation on all the three spatial scales; while their contribution in daily Tmax estimation varied over different continents or climate zones. This study was expected to improve our understanding of Ta estimation in terms of accuracy variations and influencing variables on different spatial and temporal scales. The future work mainly includes identifying underlying mechanisms of estimation errors and the uncertainty sources of Ta estimation from a local to a global scale.

scholarly journals Reduced resilience of terrestrial ecosystems locally is not reflected on a global scale

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Yuhao Feng ◽  
Haojie Su ◽  
Zhiyao Tang ◽  
Shaopeng Wang ◽  
Xia Zhao ◽  
...  
Keyword(s):  
Climate Change ◽  
Global Climate Change ◽  
Vegetation Index ◽  
Global Climate ◽  
Terrestrial Ecosystem ◽  
Terrestrial Ecosystems ◽  
Global Scale ◽  
Ecological Resilience ◽  
Terrestrial Vegetation ◽  
Ecosystem Resilience

AbstractGlobal climate change likely alters the structure and function of vegetation and the stability of terrestrial ecosystems. It is therefore important to assess the factors controlling ecosystem resilience from local to global scales. Here we assess terrestrial vegetation resilience over the past 35 years using early warning indicators calculated from normalized difference vegetation index data. On a local scale we find that climate change reduced the resilience of ecosystems in 64.5% of the global terrestrial vegetated area. Temperature had a greater influence on vegetation resilience than precipitation, while climate mean state had a greater influence than climate variability. However, there is no evidence for decreased ecological resilience on larger scales. Instead, climate warming increased spatial asynchrony of vegetation which buffered the global-scale impacts on resilience. We suggest that the response of terrestrial ecosystem resilience to global climate change is scale-dependent and influenced by spatial asynchrony on the global scale.

scholarly journals Global‐scale characterization of turning points in arid and semi‐arid ecosystem functioning

2020 ◽  
Vol 29 (7) ◽  
pp. 1230-1245 ◽  
Author(s):  
Paulo N. Bernardino ◽  
Wanda De Keersmaecker ◽  
Rasmus Fensholt ◽  
Jan Verbesselt ◽  
Ben Somers ◽  
...  
Keyword(s):  
Ecosystem Functioning ◽  
Turning Points ◽  
Global Scale ◽  
Arid Ecosystem ◽  
Scale Characterization ◽  
Semi Arid

Divergent impacts of atmospheric water demand on gross primary productivity in three typical ecosystems in China

2021 ◽  
Vol 307 ◽  
pp. 108527
Author(s):  
Ning Chen ◽  
Changchun Song ◽  
Xiaofeng Xu ◽  
Xianwei Wang ◽  
Nan Cong ◽  
...  
Keyword(s):  
Primary Productivity ◽  
Water Demand ◽  
Gross Primary Productivity ◽  
Atmospheric Water

scholarly journals Changes in Temperature and Precipitation Extremes in Superparameterized CAM in Response to Warmer SSTs

2017 ◽  
Vol 30 (24) ◽  
pp. 9827-9845 ◽  
Author(s):  
Xin Zhou ◽  
Marat F. Khairoutdinov
Keyword(s):  
Large Scale ◽  
Rain Rate ◽  
Precipitable Water ◽  
Heavy Precipitation ◽  
Global Scale ◽  
Precipitation Extremes ◽  
Temperature And Precipitation ◽  
Hourly Data ◽  
Cloud Resolving Model ◽  
Cold Extremes

Subdaily temperature and precipitation extremes in response to warmer SSTs are investigated on a global scale using the superparameterized (SP) Community Atmosphere Model (CAM), in which a cloud-resolving model is embedded in each CAM grid column to simulate convection explicitly. Two 10-yr simulations have been performed using present climatological sea surface temperature (SST) and perturbed SST climatology derived from the representative concentration pathway 8.5 (RCP8.5) scenario. Compared with the conventional CAM, SP-CAM simulates colder temperatures and more realistic intensity distribution of precipitation, especially for heavy precipitation. The temperature and precipitation extremes have been defined by the 99th percentile of the 3-hourly data. For temperature, the changes in the warm and cold extremes are generally consistent between CAM and SP-CAM, with larger changes in warm extremes at low latitudes and larger changes in cold extremes at mid-to-high latitudes. For precipitation, CAM predicts a uniform increase of frequency of precipitation extremes regardless of the rain rate, while SP-CAM predicts a monotonic increase of frequency with increasing rain rate and larger change of intensity for heavier precipitation. The changes in 3-hourly and daily temperature extremes are found to be similar; however, the 3-hourly precipitation extremes have a significantly larger change than daily extremes. The Clausius–Clapeyron scaling is found to be a relatively good predictor of zonally averaged changes in precipitation extremes over midlatitudes but not as good over the tropics and subtropics. The changes in precipitable water and large-scale vertical velocity are equally important to explain the changes in precipitation extremes.

scholarly journals Characteristics of Climate Change and Extreme Weather from 1951 to 2011 in China

2018 ◽  
Vol 15 (11) ◽  
pp. 2540 ◽  
Author(s):  
Chunli Zhao ◽  
Jianguo Chen ◽  
Peng Du ◽  
Hongyong Yuan
Keyword(s):  
Climate Change ◽  
Spatial Scale ◽  
Spatial Scales ◽  
Seasonal Fluctuation ◽  
Extreme Weather ◽  
Temperature And Precipitation ◽  
Weather Variation ◽  
Variation Characteristics ◽  
Established Fact ◽  
Temporal And Spatial

It has been demonstrated that climate change is an established fact. A good comprehension of climate and extreme weather variation characteristics on a temporal and a spatial scale is important for adaptation and response. In this work, the characteristics of temperature, precipitation, and extreme weather distribution and variation is summarized for a period of 60 years and the seasonal fluctuation of temperature and precipitation is also analyzed. The results illustrate the reduction in daily and annual temperature divergence on both temporal and spatial scales. However, the gaps remain relatively significant. Furthermore, the disparity in daily and annual precipitation are found to be increasing on both temporal and spatial scales. The findings indicate that climate change, to a certain extent, narrowed the temperature gap while widening the precipitation gap on temporal and spatial scales in China.

scholarly journals On the potential vegetation feedbacks that enhance phosphorus availability – insights from a process-based model linking geological and ecological timescales

2014 ◽  
Vol 11 (13) ◽  
pp. 3661-3683 ◽  
Author(s):  
C. Buendía ◽  
S. Arens ◽  
T. Hickler ◽  
S. I. Higgins ◽  
P. Porada ◽  
...  
Keyword(s):  
Biomass Production ◽  
Primary Productivity ◽  
Chemical Weathering ◽  
Production Efficiency ◽  
Root Exudation ◽  
Terrestrial Ecosystems ◽  
Global Scale ◽  
P Uptake ◽  
P Availability ◽  
P Limitation

Abstract. In old and heavily weathered soils, the availability of P might be so small that the primary production of plants is limited. However, plants have evolved several mechanisms to actively take up P from the soil or mine it to overcome this limitation. These mechanisms involve the active uptake of P mediated by mycorrhiza, biotic de-occlusion through root clusters, and the biotic enhancement of weathering through root exudation. The objective of this paper is to investigate how and where these processes contribute to alleviate P limitation on primary productivity. To do so, we propose a process-based model accounting for the major processes of the carbon, water, and P cycles including chemical weathering at the global scale. Implementing P limitation on biomass synthesis allows the assessment of the efficiencies of biomass production across different ecosystems. We use simulation experiments to assess the relative importance of the different uptake mechanisms to alleviate P limitation on biomass production. We find that active P uptake is an essential mechanism for sustaining P availability on long timescales, whereas biotic de-occlusion might serve as a buffer on timescales shorter than 10 000 yr. Although active P uptake is essential for reducing P losses by leaching, humid lowland soils reach P limitation after around 100 000 yr of soil evolution. Given the generalized modelling framework, our model results compare reasonably with observed or independently estimated patterns and ranges of P concentrations in soils and vegetation. Furthermore, our simulations suggest that P limitation might be an important driver of biomass production efficiency (the fraction of the gross primary productivity used for biomass growth), and that vegetation on old soils has a smaller biomass production rate when P becomes limiting. With this study, we provide a theoretical basis for investigating the responses of terrestrial ecosystems to P availability linking geological and ecological timescales under different environmental settings.

scholarly journals Applying Geospatial Semantic Array Programming for a Reproducible Set of Bioclimatic Indices in Europe

2014 ◽  
Author(s):  
Giovanni Caudullo
Keyword(s):  
Mathematical Formulation ◽  
Open Data ◽  
Global Scale ◽  
Ecological Niches ◽  
European Continent ◽  
Bioclimatic Indices ◽  
Temperature And Precipitation ◽  
Food And Agriculture ◽  
Fuzzy Similarity ◽  
Terrain Elevation

Bioclimate-driven regression analysis is a widely used approach for modelling ecological niches and zonation. Although the bioclimatic complexity of the European continent is high, a particular combination of 12 climatic and topographic covariates was recently found able to reliably reproduce the ecological zoning of the Food and Agriculture Organization of the United Nations (FAO) for forest resources assessment at pan-European scale, generating the first fuzzy similarity map of FAO ecozones in Europe. The reproducible procedure followed to derive this collection of bioclimatic indices is now presented. It required an integration of data-transformation modules (D-TM) using geospatial tools such as Geographic Information System (GIS) software, and array-based mathematical implementation such as semantic array programming (SemAP). Base variables, intermediate and final covariates are described and semantically defined by providing the workflow of D-TMs and the mathematical formulation following the SemAP notation. Source layers to derive base variables were extracted by exclusively relying on global-scale public open geodata in order for the same set of bioclimatic covariates to be reproducible in any region worldwide. In particular, two freely available datasets were exploited for temperature and precipitation (WorldClim) and elevation (Global Multi-resolution Terrain Elevation Data). The working extent covers the European continent to the Urals with a resolution of 30 arc-second. The proposed set of bioclimatic covariates will be made available as open data in the European Forest Data Centre (EFDAC). The forthcoming complete set of D-TM codelets will enable the 12 covariates to be easily reproduced and expanded through free software. .......................................................................................................................This.manuscript.has.been.accepted.for.publication.in IEEE Earthzine 2014 Vol. 7 Issue 2, 2ndquarter theme: Geospatial Semantic Array Programming. The definitive version has been published at: http://www.earthzine.org/?p=877975......................................................................................................................Please,.cite.the.definitive.version.of.the.article.as: Caudullo, G., 2014.Applying Geospatial Semantic Array Programming for a Reproducible Set of Bioclimatic Indices in Europe.IEEE Earthzine 7(2), 877975+. URL http://www.earthzine.org/?p=877975 bioRxiv pre-print doi: 10.1101/009589

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