scholarly journals An observation-constrained assessment of the climate sensitivity and future trajectories of wetland methane emissions

2020 ◽  
Vol 6 (15) ◽  
pp. eaay4444 ◽  
Author(s):  
Ernest N. Koffi ◽  
Peter Bergamaschi ◽  
Romain Alkama ◽  
Alessandro Cescatti
Keyword(s):  
Global Warming ◽  
Land Surface ◽  
Large Scale ◽  
Methane Emissions ◽  
Climate Feedbacks ◽  
Global Estimate ◽  
Temperature And Precipitation ◽  
Surface Models ◽  
Natural Wetlands

Wetlands are a major source of methane (CH4) and contribute between 30 and 40% to the total CH4 emissions. Wetland CH4 emissions depend on temperature, water table depth, and both the quantity and quality of organic matter. Global warming will affect these three drivers of methanogenesis, raising questions about the feedbacks between natural methane production and climate change. Until present the large-scale response of wetland CH4 emissions to climate has been investigated with land-surface models that have produced contrasting results. Here, we produce a novel global estimate of wetland methane emissions based on atmospheric inverse modeling of CH4 fluxes and observed temperature and precipitation. Our data-driven model suggests that by 2100, current emissions may increase by 50% to 80%, which is within the range of 50% and 150% reported in previous studies. This finding highlights the importance of limiting global warming below 2°C to avoid substantial climate feedbacks driven by methane emissions from natural wetlands.

scholarly journals Using data assimilation to optimize pedotransfer functions using large-scale in-situ soil moisture observations

2020 ◽  
Author(s):  
Elizabeth Cooper ◽  
Eleanor Blyth ◽  
Hollie Cooper ◽  
Rich Ellis ◽  
Ewan Pinnington ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Land Surface ◽  
Large Scale ◽  
Pedotransfer Functions ◽  
Surface Model ◽  
Climate Projections ◽  
Land Surface Models ◽  
Surface Models

Abstract. Soil moisture predictions from land surface models are important in hydrological, ecological and meteorological applications. In recent years the availability of wide-area soil-moisture measurements has increased, but few studies have combined model-based soil moisture predictions with in-situ observations beyond the point scale. Here we show that we can markedly improve soil moisture estimates from the JULES land surface model using field scale observations and data assimilation techniques. Rather than directly updating soil moisture estimates towards observed values, we optimize constants in the underlying pedotransfer functions, which relate soil texture to JULES soil physics parameters. In this way we generate a single set of newly calibrated pedotransfer functions based on observations from a number of UK sites with different soil textures. We demonstrate that calibrating a pedotransfer function in this way can improve the performance of land surface models, leading to the potential for better flood, drought and climate projections.

scholarly journals Do land surface models need to include differential plant species responses to drought? Examining model predictions across a mesic-xeric gradient in Europe

2015 ◽  
Vol 12 (24) ◽  
pp. 7503-7518 ◽  
Author(s):  
M. G. De Kauwe ◽  
S.-X. Zhou ◽  
B. E. Medlyn ◽  
A. J. Pitman ◽  
Y.-P. Wang ◽  
...  
Keyword(s):  
Land Surface ◽  
Large Scale ◽  
Climate Models ◽  
Soil Water Potential ◽  
Abrupt Onset ◽  
Future Climate Change ◽  
Land Surface Models ◽  
Drought Sensitivity ◽  
Coupled Climate Models ◽  
Surface Models

Abstract. Future climate change has the potential to increase drought in many regions of the globe, making it essential that land surface models (LSMs) used in coupled climate models realistically capture the drought responses of vegetation. Recent data syntheses show that drought sensitivity varies considerably among plants from different climate zones, but state-of-the-art LSMs currently assume the same drought sensitivity for all vegetation. We tested whether variable drought sensitivities are needed to explain the observed large-scale patterns of drought impact on the carbon, water and energy fluxes. We implemented data-driven drought sensitivities in the Community Atmosphere Biosphere Land Exchange (CABLE) LSM and evaluated alternative sensitivities across a latitudinal gradient in Europe during the 2003 heatwave. The model predicted an overly abrupt onset of drought unless average soil water potential was calculated with dynamic weighting across soil layers. We found that high drought sensitivity at the most mesic sites, and low drought sensitivity at the most xeric sites, was necessary to accurately model responses during drought. Our results indicate that LSMs will over-estimate drought impacts in drier climates unless different sensitivity of vegetation to drought is taken into account.

WHAT STATE OF THE NATURAL ENVIRONMENT WILL WE LEAVE FOR FUTURE GENERATIONS? REVIEW OF THE CAUSES OF THE CURRENT ECOLOGICAL CRISIS AND THE DEFINITION OF ECO-CRIMINOLOGY

2020 ◽  
Vol 138 (2) ◽  
pp. 187-200
Author(s):  
ALEKSANDRA NOWAK
Keyword(s):  
Global Warming ◽  
Natural Environment ◽  
Industrial Development ◽  
Large Scale ◽  
Ecological Crisis ◽  
Future Generations ◽  
Environmental Crisis ◽  
Definition Of ◽  
Global Population

The article provides an overview of the causes of the current environmental crisis, with three main sources being identifi ed: industrial development and large-scale agriculture, a rapidly growing global population, and environmental crime.The most serious environmental problems, such as global warming, air and soil pollution have also been characterised. The author briefl y charac-terises the defi nitions related to eco-criminology. The international commu-nity, governments, and NGOs are involved in improving the effectiveness of police cooperation in the fi ght against eco-crime, but it is still not effective enough.Environmental protection is currently one of the most important issues that humanity must address. The quality of our lives and maybe our surviv-al depend on it.

scholarly journals Do land surface models need to include differential plant species responses to drought? Examining model predictions across a latitudinal gradient in Europe

2015 ◽  
Vol 12 (15) ◽  
pp. 12349-12393 ◽  
Author(s):  
M. G. De Kauwe ◽  
S.-X. Zhou ◽  
B. E. Medlyn ◽  
A. J. Pitman ◽  
Y.-P. Wang ◽  
...  
Keyword(s):  
Land Surface ◽  
Large Scale ◽  
Latitudinal Gradient ◽  
Climate Models ◽  
Soil Water Potential ◽  
Abrupt Onset ◽  
Future Climate Change ◽  
Land Surface Models ◽  
Drought Sensitivity ◽  
Surface Models

Abstract. Future climate change has the potential to increase drought in many regions of the globe, making it essential that land surface models (LSMs) used in coupled climate models, realistically capture the drought responses of vegetation. Recent data syntheses show that drought sensitivity varies considerably among plants from different climate zones, but state-of-the-art LSMs currently assume the same drought sensitivity for all vegetation. We tested whether variable drought sensitivities are needed to explain the observed large-scale patterns of drought impact. We implemented data-driven drought sensitivities in the Community Atmosphere Biosphere Land Exchange (CABLE) LSM and evaluated alternative sensitivities across a latitudinal gradient in Europe during the 2003 heatwave. The model predicted an overly abrupt onset of drought unless average soil water potential was calculated with dynamic weighting across soil layers. We found that high drought sensitivity at the northernmost sites, and low drought sensitivity at the southernmost sites, was necessary to accurately model responses during drought. Our results indicate that LSMs will over-estimate drought impacts in drier climates unless different sensitivity of vegetation to drought is taken into account.

scholarly journals Evaluation of Drydown Processes in Global Land Surface and Hydrological Models Using Flux Tower Evapotranspiration

Water ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 356 ◽  
Author(s):  
Alberto Martínez-de la Torre ◽  
Eleanor Blyth ◽  
Emma Robinson
Keyword(s):  
Land Surface ◽  
Large Scale ◽  
Vegetation Type ◽  
Specific Model ◽  
Rainfall Event ◽  
Rate Of Change ◽  
Hydrological Models ◽  
Land Surface Models ◽  
Flux Tower ◽  
Surface Models

A key aspect of the land surface response to the atmosphere is how quickly it dries after a rainfall event. It is key because it will determine the intensity and speed of the propagation of drought and also affects the atmospheric state through changes in the surface heat exchanges. Here, we test the theory that this response can be studied as an inherent property of the land surface that is unchanging over time unless the above- and below-ground structures change. This is important as a drydown metric can be used to evaluate a landscape and its response to atmospheric drivers in models used in coupled land–atmosphere mode when the forcing is often not commensurate with the actual atmosphere. We explore whether the speed of drying of a land unit can be quantified and how this can be used to evaluate models. We use the most direct observation of drying: the rate of change of evapotranspiration after a rainfall event using eddy-covariance observations, or commonly referred to as flux tower data. We analyse the data and find that the drydown timescale is characteristic of different land cover types, then we use that to evaluate a suite of global hydrological and land surface models. We show that, at the site level, the data suggest that evapotranspiration decay timescales are longer for trees than for grasslands. The studied model’s accuracy to capture the site drydown timescales depends on the specific model, the site, and the vegetation cover representation. A more robust metric is obtained by grouping the modeled data by vegetation type and, using this, we find that land surface models capture the characteristic timescale difference between trees and grasslands, found using flux data, better than large-scale hydrological models. We thus conclude that the drydown metric has value in understanding land–atmosphere interactions and model evaluation.

Habitat suitability modeling based on remote sensing to realize time synchronization of species and environmental variables

2020 ◽  
Author(s):  
Da-Ju Wang ◽  
Hai-Yan Wei ◽  
Xu-Hui Zhang ◽  
Ya-Qin Fang ◽  
Wei Gu
Keyword(s):  
Remote Sensing ◽  
Habitat Suitability ◽  
Land Surface ◽  
Environmental Variables ◽  
Large Scale ◽  
Time Synchronization ◽  
Ambrosia Artemisiifolia ◽  
Real World Data ◽  
Habitat Suitability Modeling ◽  
Temperature And Precipitation

Abstract Aims Remote sensing (RS) is a technical method for effectively capturing real-world data on a large scale. We aimed to (i) realize the time synchronization of species and environmental variables, and extract variables related to the actual growth of species based on RS in habitat suitability modeling, and (ii) provide a reference for species management. Methods Taking invasive species Ambrosia artemisiifolia in China as an example for habitat suitability modeling. Temperature and precipitation variables were calculated from the land surface temperature (LST) provided by the moderate-resolution imaging spectroradiometer (MODIS), and climate station data, respectively. Besides, other variables that directly affect the growth or reproduction of A. artemisiifolia were also included, such as the relative humidity of the previous year's flowering period (RHPFP), and the effective UV irradiance reaching the Earth's surface (UVI). The Random Forest (RF) method was selected to model the habitat suitability. The environmental variables and samples were divided into four-time periods (i.e. 1990-2000, 2001-2005, 2006-2010, and 2011-2016) based on sampling time. Variables from the long-time series of RS (1990-2016) and WorldClim (1960-1990) were also modeled. Important Findings It was feasible to extract environmental variables from RS for habitat suitability modeling, and was more accurate than that based on the variables from WorldClim. The potential distribution of A. artemisiifolia in 1990-2000 and 2006-2010 was smaller than that in 2001-2005 and 2011-2016. The precipitation of driest months (bio14), precipitation coefficient of variation (bio15), RHPFP, and UVI were the important environmental variables that affect the growth and reproduction of A. artemisiifolia. The results indicated that the time synchronization of species and environmental variables improved the prediction accuracy of A. artemisiifolia, which should be considered in habitat suitability modeling (especially for annual species). This study can provide an important reference for the management and prevention of the spread of A. artemisiifolia.

scholarly journals Influence of Bias Correcting Predictors on Statistical Downscaling Models

2017 ◽  
Vol 56 (1) ◽  
pp. 5-26 ◽  
Author(s):  
Mathieu Vrac ◽  
Pradeebane Vaittinada Ayar
Keyword(s):  
Statistical Downscaling ◽  
Large Scale ◽  
Local Scale ◽  
Statistical Properties ◽  
Historical Period ◽  
Climate Projections ◽  
Spatial Structures ◽  
Temperature And Precipitation ◽  
Time Period

AbstractStatistical downscaling models (SDMs) and bias correction (BC) methods are commonly used to provide regional or debiased climate projections. However, most SDMs are utilized in a “perfect prognosis” context, meaning that they are calibrated on reanalysis predictors before being applied to GCM simulations. If the latter are biased, SDMs might suffer from discrepancies with observations and therefore provide unrealistic projections. It is then necessary to study the influence of applying bias correcting to large-scale predictors for SDMs, since it can have impacts on the local-scale simulations: such an investigation for daily temperature and precipitation is the goal of this study. Hence, four temperature and three precipitation SDMs are calibrated over a historical period. First, the SDMs are forced by historical predictors from two GCMs, corrected or not corrected. The two types of simulations are compared with reanalysis-driven SDM outputs to characterize the quality of the simulations. Second, changes in basic statistical properties of the raw GCM projections and those of the SDM simulations—driven by bias-corrected or raw predictors from GCM future projections—are compared. Third, the stationarity of the SDM changes brought by the BC of the predictors is investigated. Changes are computed over a historical (1976–2005) and future (2071–2100) time period and compared to assess the nonstationarity. Overall, BC can have impacts on the SDM simulations, although its influence varies from one SDM to another and from one GCM to another, with different spatial structures, and depends on the considered statistical properties. Nevertheless, corrected predictors generally improve the historical projections and can impact future evolutions with potentially strong nonstationary behaviors.

scholarly journals Online dynamical downscaling of temperature and precipitation within the <i>i</i>LOVECLIM model (version 1.1)

2018 ◽  
Vol 11 (1) ◽  
pp. 453-466
Author(s):  
Aurélien Quiquet ◽  
Didier M. Roche ◽  
Christophe Dumas ◽  
Didier Paillard
Keyword(s):  
High Resolution ◽  
Land Surface ◽  
Large Scale ◽  
Climate Model ◽  
Dynamical Downscaling ◽  
Atmospheric Model ◽  
Scale Model ◽  
Model Parameters ◽  
Temperature And Precipitation ◽  
Large Scale Model

Abstract. This paper presents the inclusion of an online dynamical downscaling of temperature and precipitation within the model of intermediate complexity iLOVECLIM v1.1. We describe the following methodology to generate temperature and precipitation fields on a 40 km  ×  40 km Cartesian grid of the Northern Hemisphere from the T21 native atmospheric model grid. Our scheme is not grid specific and conserves energy and moisture in the same way as the original climate model. We show that we are able to generate a high-resolution field which presents a spatial variability in better agreement with the observations compared to the standard model. Although the large-scale model biases are not corrected, for selected model parameters, the downscaling can induce a better overall performance compared to the standard version on both the high-resolution grid and on the native grid. Foreseen applications of this new model feature include the improvement of ice sheet model coupling and high-resolution land surface models.

scholarly journals Representation of Water Management in Hydrological and Land Surface Models

2019 ◽  
Author(s):  
Fuad Yassin ◽  
Saman Razavi ◽  
Mohamed Elshamy ◽  
Bruce Davison ◽  
Gonzalo Sapriza-Azuri ◽  
...  
Keyword(s):  
Case Studies ◽  
Land Surface ◽  
Reservoir Operation ◽  
Large Scale ◽  
Model Parameters ◽  
Land Surface Models ◽  
Operation Model ◽  
Reservoir Model ◽  
Simulation Accuracy ◽  
Surface Models

Abstract. Reservoirs significantly affect flow regimes in watershed systems by changing the magnitude and timing of streamflows. Failure to represent these effects limits the performance of hydrological and land surface models (H-LSMs) in the many highly regulated basins across the globe and limits the applicability of such models to investigate the futures of watershed systems through scenario analysis (e.g., scenarios of climate, land use, or reservoir regulation changes). An adequate representation of reservoirs and their operation in an H-LSM is therefore essential for a realistic representation of the downstream flow regime. In this paper, we present a general parametric reservoir operation model based on piecewise linear relationships between reservoir storage, inflow, and release, to approximate actual reservoir operations. For the identification of the model parameters, we propose two strategies: (a) a generalized parameterization that requires a relatively limited amount of data; and (b) direct calibration via multi-objective optimization when more data on historical storage and release are available. We use data from 37 reservoir case studies located in several regions across the globe for developing and testing the model. We further build this reservoir operation model into the MESH modelling system, which is a large-scale H-LSM. Our results across the case studies show that the proposed reservoir model with both of the parameter identification strategies leads to improved simulation accuracy compared with the other widely used approaches for reservoir operation simulation. We further show the significance of enabling MESH with this reservoir model and discuss the interdependent effects of the simulation accuracy of natural processes and that of reservoir operation on the overall model performance. The reservoir operation model is generic and can be integrated into any H-LSM.

scholarly journals Averaging over spatiotemporal heterogeneity substantially biases evapotranspiration rates in a mechanistic large-scale land evaporation model

2020 ◽  
Author(s):  
Elham Rouholahnejad Freund ◽  
Massimiliano Zappa ◽  
James W. Kirchner
Keyword(s):  
Land Surface ◽  
Large Scale ◽  
Hydrological Cycle ◽  
Land Surface Model ◽  
Aggregation Bias ◽  
Surface Model ◽  
Land Surface Models ◽  
Evaporation Model ◽  
Surface Models ◽  
Large Scale Land

Abstract. Evapotranspiration (ET) influences land-climate interactions, regulates the hydrological cycle, and contributes to the Earth's energy balance. Due to its feedbacks to large-scale hydrological processes and its impact on atmospheric dynamics, ET is a key driver of droughts and heatwaves. Existing land surface models differ substantially, both in their estimates of current ET fluxes and in their projections of how ET will evolve in the future. Any bias in estimated ET fluxes will affect the partitioning between sensible and latent heat, and thus alter model predictions of temperature and precipitation. One potential source of bias is the so-called aggregation bias that arises whenever nonlinear processes, such as those that regulate ET fluxes, are modeled using averages of heterogeneous inputs. Here we demonstrate a general mathematical approach to quantifying and correcting for this aggregation bias, using the GLEAM land evaporation model as a relatively simple example. We demonstrate that this aggregation bias can lead to substantial overestimates in ET fluxes in a typical large-scale land surface model when sub-grid heterogeneities in land surface properties are averaged out. Using Switzerland as a test case, we examine the scale-dependence of this aggregation bias and show that it can lead to overestimation of daily ET fluxes by as much as 21 % averaged over the whole country. We show how our approach can be used to identify the dominant drivers of aggregation bias, and to estimate sub-grid closure relationships that can correct for aggregation biases in ET estimates, without explicitly representing sub-grid heterogeneities in large-scale land surface models.

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