Climatic drivers and biogeophysical feedbacks: a causal inference approach over multiple temporal scales

2020 ◽  
Author(s):  
Jeroen Claessen ◽  
Annalisa Molini ◽  
Brecht Martens ◽  
Matteo Detto ◽  
Matthias Demuzere ◽  
...  
Keyword(s):  
Coupled Model ◽  
Global Scale ◽  
Cmip5 Models ◽  
Climate Control ◽  
Temporal Scales ◽  
Area Index ◽  
Local Climate ◽  
Northern Latitudes ◽  
Climatic Drivers ◽  
The Impact

<p>Earth system models (ESMs) need to correctly simulate the impact of climate on vegetation, as well as the feedback of vegetation on climate. Improving the skill of ESMs in representing climate—biosphere interactions is crucial to enhance predictions of climate and ecosystem functioning. Correlation and regression techniques are commonly used to study these interactions statistically, but these methods lack the ability to unravel the bidirectional nature of the climate–biosphere system. Here, we explore these interactions across multiple temporal scales by adopting a spectral Granger causality framework that allows identifying potentially inter-dependent variables. Multi-decadal remotely-sensed records are used to analyse the impact of key climatic drivers (precipitation, radiation and temperature) on vegetation (Leaf Area Index, LAI), as well as the biophysical feedback on local climate. These observational results are in turn used to benchmark a set of Coupled Model Intercomparison Project Phase 5 (CMIP5) members at the global scale.</p><p>Results show that the climate control on LAI variability increases with longer temporal scales, being the highest at inter-annual scales. Globally, precipitation is the most dominant driver of vegetation at monthly scales, particularly in (semi-)arid regions, as expected. The seasonal LAI variability in energy-driven latitudes is mainly controlled by radiation, while air temperature controls vegetation growth and decay in northern latitudes at inter-annual scales. ESMs have a tendency to over-represent the climate control on LAI dynamics, and especially the role of precipitation at inter-annual scales. Likewise, the widespread effect of LAI variability on radiation, as observed over the northern latitudes due to albedo changes, is also overestimated by the CMIP5 models. Overall, our experiments emphasise the potential of benchmarking the representation of climate—biosphere interactions in online ESMs using causal statistics in combination with observational data.</p>

scholarly journals Global biosphere–climate interaction: a causal appraisal of observations and models over multiple temporal scales

2019 ◽  
Vol 16 (24) ◽  
pp. 4851-4874 ◽  
Author(s):  
Jeroen Claessen ◽  
Annalisa Molini ◽  
Brecht Martens ◽  
Matteo Detto ◽  
Matthias Demuzere ◽  
...  
Keyword(s):  
Air Temperature ◽  
Arid Regions ◽  
Climate Impacts ◽  
Cmip5 Models ◽  
Climate Control ◽  
Temporal Scales ◽  
Local Climate ◽  
Northern Latitudes ◽  
The Impact ◽  
Semi Arid

Abstract. Improving the skill of Earth system models (ESMs) in representing climate–vegetation interactions is crucial to enhance our predictions of future climate and ecosystem functioning. Therefore, ESMs need to correctly simulate the impact of climate on vegetation, but likewise feedbacks of vegetation on climate must be adequately represented. However, model predictions at large spatial scales remain subjected to large uncertainties, mostly due to the lack of observational patterns to benchmark them. Here, the bidirectional nature of climate–vegetation interactions is explored across multiple temporal scales by adopting a spectral Granger causality framework that allows identification of potentially co-dependent variables. Results based on global and multi-decadal records of remotely sensed leaf area index (LAI) and observed atmospheric data show that the climate control on vegetation variability increases with longer temporal scales, being higher at inter-annual than multi-month scales. Globally, precipitation is the most dominant driver of vegetation at monthly scales, particularly in (semi-)arid regions. The seasonal LAI variability in energy-driven latitudes is mainly controlled by radiation, while air temperature controls vegetation growth and decay in high northern latitudes at inter-annual scales. These observational results are used as a benchmark to evaluate four ESM simulations from the Coupled Model Intercomparison Project Phase 5 (CMIP5). Findings indicate a tendency of ESMs to over-represent the climate control on LAI dynamics and a particular overestimation of the dominance of precipitation in arid and semi-arid regions at inter-annual scales. Analogously, CMIP5 models overestimate the control of air temperature on seasonal vegetation variability, especially in forested regions. Overall, climate impacts on LAI are found to be stronger than the feedbacks of LAI on climate in both observations and models; in other words, local climate variability leaves a larger imprint on temporal LAI dynamics than vice versa. Note however that while vegetation reacts directly to its local climate conditions, the spatially collocated character of the analysis does not allow for the identification of remote feedbacks, which might result in an underestimation of the biophysical effects of vegetation on climate. Nonetheless, the widespread effect of LAI variability on radiation, as observed over the northern latitudes due to albedo changes, is overestimated by the CMIP5 models. Overall, our experiments emphasise the potential of benchmarking the representation of particular interactions in online ESMs using causal statistics in combination with observational data, as opposed to the more conventional evaluation of the magnitude and dynamics of individual variables.

scholarly journals Global biosphere–climate interaction: a multi-scale appraisal of observations and models

2019 ◽  
Author(s):  
Jeroen Claessen ◽  
Annalisa Molini ◽  
Brecht Martens ◽  
Matteo Detto ◽  
Matthias Demuzere ◽  
...  
Keyword(s):  
Spatial Scales ◽  
Climate Impacts ◽  
Cmip5 Models ◽  
Wide Spread ◽  
Climate Control ◽  
Temporal Scales ◽  
Area Index ◽  
Local Character ◽  
The Impact ◽  
Semi Arid

Abstract. Improving the skill of Earth System Models (ESMs) in representing climate–vegetation interactions is crucial to enhance our predictions of future climate and ecosystem functioning. Therefore, ESMs need to correctly simulate the impact of climate on vegetation, but likewise, feedbacks of vegetation on climate must be adequately represented. However, model predictions at large spatial scales remain subjected to large uncertainties, mostly due to the lack of observational patterns to benchmark them. Here, the bi-directional nature of climate–vegetation interactions is explored across multiple temporal scales by adopting a spectral Granger causality framework that allows identifying potentially co-dependent variables. Results based on global and multi-decadal records of remotely-sensed leaf area index (LAI) and observed atmospheric data show that the climate control on vegetation variability increases with longer temporal scales, being higher at inter-annual than multi-month scales. The phenological cycle in energy-driven latitudes is mainly controlled by radiation, while in (semi-)arid regimes precipitation variability dominates at all temporal scales. However, at inter-annual scales, the control of water availability gradually becomes more wide-spread than that of energy constraints. The observational results are used as a benchmark to evaluate ESM simulations from the Coupled Model Intercomparison Project Phase 5 (CMIP5). Findings indicate a tendency of ESMs to over-represent the climate control on LAI dynamics, and a particular overestimation of the dominance of precipitation in arid and semi-arid regions. Analogously, CMIP5 models overestimate the control of air temperature on forest seasonal phenology. Overall, climate impacts on LAI are found to be stronger than the feedbacks of LAI on climate in both observations and models, arguably due to the local character of the analysis that does not allow for the identification of downwind or remote vegetation feedbacks. Nonetheless, wide-spread effects of LAI variability on radiation are observed over the northern latitudes, presumably related to albedo changes, which are well-captured by the CMIP5 models. Overall, our experiments emphasise the potential of benchmarking the representation of particular interactions in online ESMs using causal statistics in combination with observational data, as opposed to the more conventional evaluation of the magnitude and dynamics of individual variables.

scholarly journals The impact of air–sea coupling and ocean biases on the seasonal cycle of southern West African precipitation

2019 ◽  
Vol 53 (11) ◽  
pp. 7027-7044
Author(s):  
Caroline M. Wainwright ◽  
Linda C. Hirons ◽  
Nicholas P. Klingaman ◽  
Richard P. Allan ◽  
Emily Black ◽  
...  
Keyword(s):  
West Africa ◽  
Atlantic Ocean ◽  
Crop Yields ◽  
Coupled Model ◽  
Dry Season ◽  
Global Ocean ◽  
Cmip5 Models ◽  
Rainfall Regime ◽  
Climate Projections ◽  
The Impact

Abstract The biannual seasonal rainfall regime over the southern part of West Africa is characterised by two wet seasons, separated by the ‘Little Dry Season’ in July–August. Lower rainfall totals during this intervening dry season may be detrimental for crop yields over a region with a dense population that depends on agricultural output. Coupled Model Intercomparison Project Phase 5 (CMIP5) models do not correctly capture this seasonal regime, and instead generate a single wet season, peaking at the observed timing of the Little Dry Season. Hence, the realism of future climate projections over this region is questionable. Here, the representation of the Little Dry Season in coupled model simulations is investigated, to elucidate factors leading to this misrepresentation. The Global Ocean Mixed Layer configuration of the Met Office Unified Model is particularly useful for exploring this misrepresentation, as it enables separating the effects of coupled model ocean biases in different ocean basins while maintaining air–sea coupling. Atlantic Ocean SST biases cause the incorrect seasonal regime over southern West Africa. Upper level descent in August reduces ascent along the coastline, which is associated with the observed reduction in rainfall during the Little Dry Season. When coupled model Atlantic Ocean biases are introduced, ascent over the coastline is deeper and rainfall totals are higher during July–August. Hence, this study indicates detrimental impacts introduced by Atlantic Ocean biases, and highlights an area of model development required for production of meaningful climate change projections over the West Africa region.

scholarly journals The influence of Carboniferous palaeoatmospheres on plant function: an experimental and modelling assessment

1998 ◽  
Vol 353 (1365) ◽  
pp. 131-140 ◽  
Author(s):  
D. J. Beerling ◽  
F. I. Woodward ◽  
M. R. Lomas ◽  
M. A. Wills ◽  
W. P. Quick ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Soil Carbon ◽  
Primary Productivity ◽  
Net Primary Productivity ◽  
Global Scale ◽  
Carbon Accumulation ◽  
Late Carboniferous ◽  
Atmospheric Composition ◽  
Area Index ◽  
The Impact

Geochemical models of atmospheric evolution predict that during the late Carboniferous, ca . 300 Ma, atmospheric oxygen and carbon dioxide concentrations were 35% and 0.03%, respectively. Both gases compete with each other for ribulose–1,5–bisphosphate carboxylase/oxygenase–the primary C–fixing enzyme in C 3 land plants: and the absolute concentrations and the ratio of the two in the atmosphere have the potential to strongly influence land–plant function. The Carboniferous therefore represents an era of potentially strong feedback between atmospheric composition and plant function. We assessed some implications of this ratio of atmospheric gases on plant function using experimental and modelling approaches. After six weeks growth at 35% O 2 and 0.03% carbon dioxide, no photosynthetic acclimation was observed in the woody species Betula pubescens and Hedera helix relative to those plants grown at 21% O 2 . Leaf photosynthetic rates were 29% lower in the high O 2 environment compared to the controls. A global–scale analysis of the impact of the late Carboniferous climate and atmospheric composition on vegetation function was determined by driving a process–based vegetation–biogeochemistry model with a Carboniferous global palaeoclimate simulated by the Universities Global Atmospheric Modelling Programme General Circulation Model. Global patterns of net primary productivity, leaf area index and soil carbon concentration for the equilibrium model solutions showed generally low values everywhere, compared with the present day, except for a central band in the northern land mass extension of Gondwana, where high values were predicted. The areas of high soil carbon accumulation closely match the known distribution of late Carboniferous coals. Sensitivity analysis with the model indicated that the increase in O 2 concentration from 21% to 35% reduced global net primary productivity by 18.7% or by 6.3 GtC yr –1 . Further work is required to collate and map at the global scale the distribution of vegetation types, and evidence for wildfires, for the late Carboniferous to test our predictions.

scholarly journals Influence of changes in humidity on dry temperature in GPS RO climatologies

2014 ◽  
Vol 7 (6) ◽  
pp. 5457-5489 ◽  
Author(s):  
J. Danzer ◽  
U. Foelsche ◽  
B. Scherllin-Pirscher ◽  
M. Schwärz
Keyword(s):  
Water Vapor ◽  
Dominant Role ◽  
Lower Boundary ◽  
Coupled Model ◽  
Absolute Humidity ◽  
Background Information ◽  
Maximum Temperature ◽  
Cmip5 Models ◽  
Northern Latitudes ◽  
Influence Of Water

Abstract. Radio Occultation (RO) data are increasingly used in climate research. Accurate phase (change) measurements of Global Positioning System (GPS) signals are the basis for the retrieval of near vertical profiles of bending angle, microwave refractivity, density, pressure, and temperature. If temperature is calculated from observed refractivity with the assumption that water vapor is zero, the product is called "dry temperature", which is commonly used to study the Earth's atmosphere, e.g., when analyzing temperature trends due to global warming. Dry temperature is a useful quantity, since it does not need additional background information in its retrieval. However, it can only be safely used as proxy for physical temperature, where moisture is negligible. The altitude region above which water vapor does not play a dominant role anymore, depends primarily on latitude and season. In this study we first investigated the influence of water vapor on dry temperature RO profiles. Hence, we analyzed the maximum altitude down to which monthly mean dry temperature profiles can be regarded as being equivalent to physical temperature. This was done by examining dry temperature to physical temperature differences of monthly mean analysis fields from the European Centre for Medium-Range Weather Forecasts (ECMWF), studied from 2006 until 2010. We introduced cutoff criteria, where maximum temperature differences of −0.1, −0.05, and −0.02 K were allowed (dry temperature is always lower than physical temperature), and computed the corresponding altitudes. As an example, a temperature difference of −0.05 K in the tropics was found at an altitude of about 14 km, while at higher northern latitudes in winter it was found at an altitude of about 9 to 10 km, in summer at about 11 km. Furthermore, regarding climate change, we expect an increase of absolute humidity in the atmosphere. This possible trend in water vapor could yield a wrongly interpreted dry temperature trend. As a consequence, we performed a model study, investigating the increase in height of the transition region between moist and dry air. We used data from the fifth phase of the Coupled Model Intercomparison Project (CMIP5), analyzing again monthly mean dry temperature to physical temperature differences, now from the years 2006 to 2050. We used the highest emission scenario RCP8.5 (Representative Concentration Pathway), studying all available models of the CMIP5 project, analyzing one internal run per model, with the goal to identify the altitude region where trends in dry temperature can be safely regarded as reflecting trends in physical temperature. From all models we therefore choose a selection of models ("max 8" CMIP5 models), which showed the largest trend differences. As a result, our trend study suggests that the lower boundary of the region where dry temperature is essentially equal to physical temperature rises about 150 m decade−1.

scholarly journals Contribution of Dynamic Vegetation Phenology to Decadal Climate Predictability

2014 ◽  
Vol 27 (22) ◽  
pp. 8563-8577 ◽  
Author(s):  
Martina Weiss ◽  
Paul A. Miller ◽  
Bart J. J. M. van den Hurk ◽  
Twan van Noije ◽  
Simona Ştefănescu ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Degrees Of Freedom ◽  
Reference Model ◽  
Coupled Model ◽  
Coupled System ◽  
Cold Bias ◽  
Area Index ◽  
Skill Scores ◽  
The Impact

Abstract In this study, the impact of coupling and initializing the leaf area index from the dynamic vegetation model Lund–Potsdam–Jena General Ecosystem Simulator (LPJ-GUESS) is analyzed on skill of decadal predictions in the fully coupled atmosphere–land–ocean–sea ice model, the European Consortium Earth System Model (EC-Earth). Similar to the impact of initializing the model with the observed oceanic state, initializing the leaf area index (LAI) fields obtained from an offline LPJ-GUESS simulation forced by the observed atmospheric state leads to a systematic drift. A different treatment of the water and soil moisture budget in LPJ-GUESS is a likely cause of this drift. The coupled system reduces the cold bias of the reference model over land by reducing LAI (and the associated evaporative cooling), particularly outside the growing season. The coupling with the interactive vegetation module implies more degrees of freedom in the coupled model, which generates more noise that can mask a portion of the extra signal that is generated. The forecast reliability improves marginally, particularly early in the forecast. Ranked probability skill scores are also improved slightly in most areas analyzed, but the signal is not fully coherent over the forecast interval because of the relatively low number of ensemble members. Methods to remove the LAI drift and allow coupling of other variables probably need to be implemented before significant forecast skill can be expected.

scholarly journals Projection of North Pacific Tropical Upper-Tropospheric Trough in CMIP5 Models: Implications for Changes in Tropical Cyclone Formation Locations

2018 ◽  
Vol 31 (2) ◽  
pp. 761-774 ◽  
Author(s):  
Chao Wang ◽  
Liguang Wu
Keyword(s):  
Tropical Cyclone ◽  
North Pacific ◽  
Climate Models ◽  
Coupled Model ◽  
Atmospheric Composition ◽  
Cmip5 Models ◽  
The North ◽  
The Mean ◽  
The Impact ◽  
The North Pacific

The strong westerly shear to the south flank of the tropical upper-tropospheric trough (TUTT) limits the eastward extension of tropical cyclone (TC) formation over the western North Pacific (WNP) and thus the zonal shift of the TUTT in warming scenarios has an important implication for the mean formation location of TCs. The impact of global warming on the zonal shift of the TUTT is investigated by using output from phase 5 of the Coupled Model Intercomparison Project (CMIP5) of 36 climate models in this study. It is found that considerable spread exists in the zonal position, orientation, and intensity of the simulated-climatologic TUTT in the historical runs, which is forced by observed conditions such as changes in atmospheric composition, solar forcing, and aerosols. The large spread is closely related to the diversity in the simulated SST biases over the North Pacific. Based on the 15 models with relatively high skill in their historical runs, the near-term (2016–35) projection shows no significant change of the TUTT longitude, while the TUTT experiences an eastward shift of 1.9° and 3.2° longitude in the representative concentration pathway (RCP) 4.5 and 8.5 scenarios in the long-term (2081–2100) projection with considerable intermodel variability. Further examination indicates that the projected changes in the zonal location of the TUTT are also associated with the projected relative SST anomalies over the North Pacific. A stronger (weaker) relative SST warming over the North Pacific favors an eastward (westward) shift of the TUTT, suggesting that the spatial pattern of the future SST change is an important factor for the zonal shift of the mean formation location of TCs.

scholarly journals Caracterización de la fenología de la vegetación a escala global mediante series temporales SPOT VEGETATION

2016 ◽  
pp. 1
Author(s):  
A. Verger ◽  
I. Filella ◽  
F. Baret ◽  
J. Peñuelas
Keyword(s):  
Time Series ◽  
Land Surface ◽  
Global Scale ◽  
Latitudinal Gradients ◽  
Area Index ◽  
Climatic Drivers ◽  
Phenological Metrics ◽  
Earth System Models ◽  
Good Agreement ◽  
Birch Forests

<p align="justify">Land surface phenology from time series of satellite data are expected to contribute to improve the representation of vegetation phenology in earth system models. We characterized the baseline phenology of the vegetation at the global scale from GEOCLIM-LAI, a global climatology of leaf area index (LAI) derived from 1-km SPOT VEGETATION time series for 1999-2010. The calibration with ground measurements showed that the start and end of season were best identified using respectively 30% and 40% threshold of LAI amplitude values. The satellite-derived phenology was spatially consistent with the global distributions of climatic drivers and biome land cover. The accuracy of the derived phenological metrics, evaluated using available ground observations for birch forests in Europe, cherry in Asia and lilac shrubs in North America showed an overall root mean square error lower than 19 days for the start, end and length of season, and good agreement between the latitudinal gradients of VEGETATION LAI phenology and ground data.</p>

scholarly journals The Impact of Regional Multidecadal and Century-Scale Internal Climate Variability on Sea Level Trends in CMIP5 Models

2015 ◽  
Vol 28 (2) ◽  
pp. 853-861 ◽  
Author(s):  
Mark Carson ◽  
Armin Köhl ◽  
Detlef Stammer
Keyword(s):  
Sea Level ◽  
Time Scales ◽  
Coupled Model ◽  
Internal Variability ◽  
Cmip5 Models ◽  
Level Control ◽  
Ensemble Spread ◽  
Tropical Ocean ◽  
Regional Sea Level ◽  
The Impact

Abstract Regional sea surface height variability due to internal climate fluctuations is estimated using preindustrial control runs of 21 models from phase 5 of the Coupled Model Intercomparison Project (CMIP5). Projected sea level trends of the representative concentration pathway 4.5 (RCP4.5) scenario for 20-, 50-, and 100-yr intervals grow from being largely dominated by internal variability on shorter time scales to being the dominant sea level signal on long time scales. The internal variability is estimated by calculating overlapping trends for the various time scales on the regional sea level control run output from each model. When compared to the ensemble spread of the RCP4.5 scenario trends, the internal variability remains a substantial portion of the spread even after 50 years. The regional ensemble mean trends are mostly larger than the ensemble spread for the 50-yr interval and are larger everywhere, except for part of the central Arctic and the Southern Ocean for the 100-yr projection. Although it is unclear whether the model internal variability estimate will be comparable to long-term variability in the real ocean, the authors compare the strength of the estimate to satellite altimetry and find that altimetry-based trends may be larger in tropical ocean regions, with only limited extratropical regions rising above the internal variability. The authors also analyze a single model’s internal variability against its future RCP4.5-projected sea level and show that, by 50 years, many regional sea level trends are larger than the underlying internal variability, though this variability still accounts for more than a third of the trend magnitude for almost half of the extratropical ocean.

scholarly journals Moving beyond the Total Sea Ice Extent in Gauging Model Biases

2016 ◽  
Vol 29 (24) ◽  
pp. 8965-8987 ◽  
Author(s):  
Detelina P. Ivanova ◽  
Peter J. Gleckler ◽  
Karl E. Taylor ◽  
Paul J. Durack ◽  
Kate D. Marvel
Keyword(s):  
Sea Ice ◽  
Error Compensation ◽  
Climate Modeling ◽  
Model Performance ◽  
Coupled Model ◽  
Cmip5 Models ◽  
Model Errors ◽  
Sea Ice Extent ◽  
Model Biases ◽  
The Impact

Abstract Reproducing characteristics of observed sea ice extent remains an important climate modeling challenge. This study describes several approaches to improve how model biases in total sea ice distribution are quantified, and applies them to historically forced simulations contributed to phase 5 of the Coupled Model Intercomparison Project (CMIP5). The quantity of hemispheric total sea ice area, or some measure of its equatorward extent, is often used to evaluate model performance. A new approach is introduced that investigates additional details about the structure of model errors, with an aim to reduce the potential impact of compensating errors when gauging differences between simulated and observed sea ice. Using multiple observational datasets, several new methods are applied to evaluate the climatological spatial distribution and the annual cycle of sea ice cover in 41 CMIP5 models. It is shown that in some models, error compensation can be substantial, for example resulting from too much sea ice in one region and too little in another. Error compensation tends to be larger in models that agree more closely with the observed total sea ice area, which may result from model tuning. The results herein suggest that consideration of only the total hemispheric sea ice area or extent can be misleading when quantitatively comparing how well models agree with observations. Further work is needed to fully develop robust methods to holistically evaluate the ability of models to capture the finescale structure of sea ice characteristics; however, the “sector scale” metric used here aids in reducing the impact of compensating errors in hemispheric integrals.

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