scholarly journals Using the International Tree-Ring Data Bank (ITRDB) records as century-long benchmarks for global land-surface models

2021 ◽  
Vol 14 (9) ◽  
pp. 5891-5913
Author(s):  
Jina Jeong ◽  
Jonathan Barichivich ◽  
Philippe Peylin ◽  
Vanessa Haverd ◽  
Matthew Joseph McGrath ◽  
...  
Keyword(s):  
Tree Ring ◽  
Land Surface ◽  
Radial Growth ◽  
Data Bank ◽  
Land Surface Models ◽  
Mature Trees ◽  
Diameter Increment ◽  
Tree Ring Data ◽  
Surface Models

Abstract. The search for a long-term benchmark for land-surface models (LSMs) has brought tree-ring data to the attention of the land-surface modelling community, as tree-ring data have recorded growth well before human-induced environmental changes became important. We propose and evaluate an improved conceptual framework of when and how tree-ring data may, despite their sampling biases, be used as century-long hindcasting targets for evaluating LSMs. Four complementary benchmarks – size-related diameter growth, diameter increment of mature trees, diameter increment of young trees, and the response of tree growth to extreme events – were simulated using the ORCHIDEE version r5698 LSM and were verified against observations from 11 sites in the independent, unbiased European biomass network datasets. The potential for big-tree selection bias in the International Tree-Ring Data Bank (ITRDB) was investigated by subsampling the 11 sites from European biomass network. We find that in about 95 % of the test cases, using ITRDB data would result in the same conclusions as using the European biomass network when the LSM is benchmarked against the annual radial growth during extreme climate years. The ITRDB data can be used with 70 % confidence when benchmarked against the annual radial growth of mature trees or the size-related trend in annual radial growth. Care should be taken when using the ITRDB data to benchmark the annual radial growth of young trees, as only 50 % of the test cases were consistent with the results from the European biomass network. The proposed maximum tree diameter and annual growth increment benchmarks may enable the use of ITRDB data for large-scale validation of the LSM-simulated response of forest ecosystems to the transition from pre-industrial to present-day environmental conditions over the past century. The results also suggest ways in which tree-ring width observations may be collected and/or reprocessed to provide long-term validation tests for land-surface models.

scholarly journals Supplementary material to "Using the International Tree-Ring Data Bank (ITRDB) records as century-long benchmarks for land-surface models"

2020 ◽  
Author(s):  
Jina Jeong ◽  
Jonathan Barichivich ◽  
Philippe Peylin ◽  
Vanessa Haverd ◽  
Matthew J. McGrath ◽  
...  
Keyword(s):  
Tree Ring ◽  
Land Surface ◽  
Data Bank ◽  
Land Surface Models ◽  
Tree Ring Data ◽  
Surface Models ◽  
Supplementary Material

scholarly journals Using the International Tree-Ring Data Bank (ITRDB) records as century-long benchmarks for land-surface models

2020 ◽  
Author(s):  
Jina Jeong ◽  
Jonathan Barichivich ◽  
Philippe Peylin ◽  
Vanessa Haverd ◽  
Matthew J. McGrath ◽  
...  
Keyword(s):  
Data Processing ◽  
Tree Ring ◽  
Land Surface ◽  
Environmental Changes ◽  
Ring Width ◽  
Data Bank ◽  
Tree Ring Width ◽  
Land Surface Models ◽  
Tree Ring Data ◽  
Surface Models

Abstract. The search for a long-term benchmark for land-surface models (LSM) has brought tree-ring data to the attention of the land-surface community as they record growth well before human-induced environmental changes became important. The most comprehensive archive of publicly shared tree-ring data is the International Tree-ring Data Bank (ITRDB). Many records in the ITRDB have, however, been collected almost exclusively with a view on maximizing an environmental target signal (e.g. climate), which has resulted in a biased representation of forested sites and landscapes and thus limits its use as a data source for benchmarking. The aim of this study is to propose advances in land-surface modelling and data processing to enable the land-surface community to re-use the ITRDB data as a much-needed century-long benchmark. Given that tree-ring width is largely explained by phenology, tree size, and climate sensitivity, LSMs that intend to use it as a benchmark should at least simulate tree phenology, size-dependent growth, differently-sized trees within a stand, and responses to changes in temperature, precipitation and atmospheric CO2 con¬cen¬tra¬tions. Yet, even if LSMs were capable of accurately simulating tree-ring width, sampling biases in the ITRDB need to be accounted for. This study proposes two solutions: exploiting the observation that the variation due to size-related growth by far exceeds the variation due to environmental changes; and simulating a size-structured population of trees. Combining the proposed advances in modelling and data processing resulted in four complementary benchmarks - reflecting different usage of the information contained in the ITRDB - each described by two metrics rooted in statistics that quantify the performance of the benchmark. Although the proposed benchmarks are unlikely to be precise, they advance the field by providing a much-needed large-scale constraint on changes in the simulated maximum tree diameter and annual growth increment for the transition from pre-industrial to present-day environmental conditions over the past century. Hence, the proposed benchmarks open up new ways of exploring the ITRDB archive, stimulate the dendrochronological community to refine its sampling protocols to produce new and spatially unbiased tree-ring networks, and help the modelling community to move beyond the short-term benchmarking of LSM.

Developing a triple tree-ring constraint for tree growth and physiology in a global land surface model

2021 ◽  
Author(s):  
Jonathan Barichivich ◽  
Philippe Peylin ◽  
Valérie Daux ◽  
Camille Risi ◽  
Jina Jeong ◽  
...  
Keyword(s):  
Tree Ring ◽  
Land Surface ◽  
Forest Ecosystems ◽  
Ring Width ◽  
Land Surface Model ◽  
Surface Model ◽  
Growth Variability ◽  
Tree Ring Data ◽  
Surface Models ◽  
Global Land

<p>Gradual anthropogenic warming and parallel changes in the major global biogeochemical cycles are slowly pushing forest ecosystems into novel growing conditions, with uncertain consequences for ecosystem dynamics and climate. Short-term forest responses (i.e., years to a decade) to global change factors are relatively well understood and skilfully simulated by land surface models (LSMs). However, confidence on model projections weaken towards longer time scales and to the future, mainly because the long-term responses (i.e., decade to century) of these models remain unconstrained. This issue limits confidence on climate model projections. Annually-resolved tree-ring records, extending back to pre-industrial conditions, have the potential to constrain model responses at interannual to centennial time scales. Here, we constrain the representation of tree growth and physiology in the ORCHIDEE global land surface model using the simulated interannual variability of tree-ring width and carbon (Δ<sup>13</sup>C) and oxygen (δ<sup>18</sup>O) stable isotopes in six sites in boreal and temperate Europe.  The model simulates Δ<sup>13</sup>C (r = 0.31-0.80) and δ<sup>18</sup>O (r = 0.36-0.74) variability better than tree-ring width variability (r < 0.55), with an overall skill similar to that of other state-of-the-art models such as MAIDENiso and LPX-Bern. These results show that growth variability is not well represented, and that the parameterization of leaf-level physiological responses to drought stress in the temperate region can be improved with tree-ring data. The representation of carbon storage and remobilization dynamics is critical to improve the realism of simulated growth variability, temporal carrying over and recovery of forest ecosystems after climate extremes. The simulated physiological response to rising CO2 over the 20th century is consistent with tree-ring data in the temperate region, despite an overestimation of seasonal drought stress and stomatal control on photosynthesis. Photosynthesis correlates directly with isotopic variability, but correlations with δ<sup>18</sup>O combine physiological effects and climate variability impacts on source water signatures. The integration of tree-ring data (i.e. the triple constraint: width, Δ<sup>13</sup>C and δ<sup>18</sup>O) and land surface models as demonstrated here should contribute towards reducing current uncertainties in forest carbon and water cycling.</p>

scholarly journals MPR 1.0: A stand-alone Multiscale Parameter Regionalization Tool for Improved Parameter Estimation of Land Surface Models

2021 ◽  
Author(s):  
Robert Schweppe ◽  
Stephan Thober ◽  
Matthias Kelbling ◽  
Rohini Kumar ◽  
Sabine Attinger ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Parameter Space ◽  
Land Surface ◽  
Hydrologic Model ◽  
Model Parameters ◽  
Land Surface Models ◽  
Continental Scale ◽  
Environmental Models ◽  
Surface Models

Abstract. Distributed environmental models such as land surface models (LSM) require model parameters in each spatial modelling unit (e.g. grid cell), thereby leading to a high-dimensional parameter space. One approach to decrease the dimen- sionality of parameter space in these models is to use regularization techniques. One such highly efficient technique is the Multiscale Parameter Regionalization (MPR) framework that translates high-resolution predictor variables (e.g., soil textural properties) into model parameters (e.g., porosity) via transfer functions (TFs) and upscaling operators that are suitable for every modeled process. This framework yields seamless model parameters at multiple scales and locations in an effective manner. However, integration of MPR into existing modeling workflows has been hindered thus far by hard-coded configurations and non-modular software designs. For these reasons, we redesigned MPR as a model-agnostic, stand-alone tool. It is a useful software for creating graphs of netCDF variables, wherein each node is a variable and the links consist of TFs and/or upscaling operators. In this study, we present and verify our tool against a previous version, which was implemented in the mesoscale hydrologic model mHM (www.ufz.de/mhm). By using this tool for the generation of continental-scale soil hydraulic param- eters applicable to different models (Noah-MP and HTESSEL), we showcase its general functionality and flexibility. Further, using model parameters estimated by the MPR tool leads to significant changes in long-term estimates of evapotranspiration, as compared to their default parameterizations. For example, a change of up to 25 % in long-term evapotranspiration flux is observed in Noah-MP and HTESSEL in the Mississippi River basin. We postulate that use of the stand-alone MPR tool will considerably increase the transparency and reproducibility of the parameter estimation process in distributed (environmental) models. It will also allow a rigorous uncertainty estimation related to the errors of the predictors (e.g., soil texture fields), transfer function and its parameters, and remapping (or upscaling) algorithms.

Simulation of Global Land Surface Conditions from 1948 to 2004. Part I: Forcing Data and Evaluations

2006 ◽  
Vol 7 (5) ◽  
pp. 953-975 ◽  
Author(s):  
Taotao Qian ◽  
Aiguo Dai ◽  
Kevin E. Trenberth ◽  
Keith W. Oleson
Keyword(s):  
Soil Moisture ◽  
Air Temperature ◽  
Land Surface ◽  
Surface Air Temperature ◽  
Reanalysis Data ◽  
Freshwater Discharge ◽  
Land Surface Models ◽  
Surface Conditions ◽  
Surface Models

Abstract Because of a lack of observations, historical simulations of land surface conditions using land surface models are needed for studying variability and changes in the continental water cycle and for providing initial conditions for seasonal climate predictions. Atmospheric forcing datasets are also needed for land surface model development. The quality of atmospheric forcing data greatly affects the ability of land surface models to realistically simulate land surface conditions. Here a carefully constructed global forcing dataset for 1948–2004 with 3-hourly and T62 (∼1.875°) resolution is described, and historical simulations using the latest version of the Community Land Model version 3.0 (CLM3) are evaluated using available observations of streamflow, continental freshwater discharge, surface runoff, and soil moisture. The forcing dataset was derived by combining observation-based analyses of monthly precipitation and surface air temperature with intramonthly variations from the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis, which is shown to have spurious trends and biases in surface temperature and precipitation. Surface downward solar radiation from the reanalysis was first adjusted for variations and trends using monthly station records of cloud cover anomaly and then for mean biases using satellite observations during recent decades. Surface specific humidity from the reanalysis was adjusted using the adjusted surface air temperature and reanalysis relative humidity. Surface wind speed and air pressure were interpolated directly from the 6-hourly reanalysis data. Sensitivity experiments show that the precipitation adjustment (to the reanalysis data) leads to the largest improvement, while the temperature and radiation adjustments have only small effects. When forced by this dataset, the CLM3 reproduces many aspects of the long-term mean, annual cycle, interannual and decadal variations, and trends of streamflow for many large rivers (e.g., the Orinoco, Changjiang, Mississippi, etc.), although substantial biases exist. The simulated long-term-mean freshwater discharge into the global and individual oceans is comparable to 921 river-based observational estimates. Observed soil moisture variations over Illinois and parts of Eurasia are generally simulated well, with the dominant influence coming from precipitation. The results suggest that the CLM3 simulations are useful for climate change analysis. It is also shown that unrealistically low intensity and high frequency of precipitation, as in most model-simulated precipitation or observed time-averaged fields, result in too much evaporation and too little runoff, which leads to lower than observed river flows. This problem can be reduced by adjusting the precipitation rates using observed-precipitation frequency maps.

scholarly journals A triple tree-ring constraint for tree growth and physiology in a global land surface model

2021 ◽  
Vol 18 (12) ◽  
pp. 3781-3803
Author(s):  
Jonathan Barichivich ◽  
Philippe Peylin ◽  
Thomas Launois ◽  
Valerie Daux ◽  
Camille Risi ◽  
...  
Keyword(s):  
Tree Ring ◽  
Land Surface ◽  
Ring Width ◽  
Land Surface Model ◽  
Surface Model ◽  
Tree Ring Width ◽  
Growth Variability ◽  
Tree Ring Data ◽  
Surface Models ◽  
Global Land

Abstract. Annually resolved tree-ring records extending back to pre-industrial conditions have the potential to constrain the responses of global land surface models at interannual to centennial timescales. Here, we demonstrate a framework to simultaneously constrain the representation of tree growth and physiology in the ORCHIDEE global land surface model using the simulated variability of tree-ring width and carbon (Δ13C) and oxygen (δ18O) stable isotopes in six sites in boreal and temperate Europe. We exploit the resulting tree-ring triplet to derive integrative constraints for leaf physiology and growth from well-known mechanistic relationships among the variables. ORCHIDEE simulates Δ13C (r=0.31–0.80) and δ18O (r=0.36–0.74) better than tree-ring width (r<0.55), with an overall skill similar to that of a tree-ring model (MAIDENiso) and another isotope-enabled global vegetation model (LPX-Bern). The comparison with tree-ring data showed that growth variability is not well represented in ORCHIDEE and that the parameterization of leaf-level physiological responses (stomatal control) to drought stress in the temperate region can be constrained using the interannual variability of tree-ring stable isotopes. The representation of carbon storage and remobilization dynamics emerged as a critical process to improve the realism of simulated growth variability, temporal carryover, and recovery of forest ecosystems after climate extremes. Simulated forest gross primary productivity (GPP) correlates with simulated tree-ring Δ13C and δ18O variability, but the origin of the correlations with tree-ring δ18O is not entirely physiological. The integration of tree-ring data and land surface models as demonstrated here should guide model improvements and contribute towards reducing current uncertainties in forest carbon and water cycling.

scholarly journals Climate controlled root zone parameters show potential to improve water flux simulations by land surface models

2021 ◽  
Author(s):  
Fransje van Oorschot ◽  
Ruud J. van der Ent ◽  
Markus Hrachowitz ◽  
Andrea Alessandri
Keyword(s):  
Land Surface ◽  
Root Zone ◽  
Surface Model ◽  
Land Surface Models ◽  
Climate Control ◽  
Local Climate ◽  
Climate Conditions ◽  
Surface Models ◽  
Zone Parameters

Abstract. The root zone storage capacity Sr is the maximum volume of water in the subsurface that can potentially be accessed by vegetation for transpiration. It influences the seasonality of transpiration as well as fast and slow runoff processes. Many studies have shown that Sr is heterogeneous as controlled by local climate conditions, which affect vegetation strategies in sizing their root system able to support plant growth and to prevent water shortages. Root zone parameterization in most land surface models does not account for this climate control on root development, being based on look-up tables that prescribe worldwide the same root zone parameters for each vegetation class. These look-up tables are obtained from measurements of rooting structure that are scarce and hardly representative of the ecosystem scale. The objective of this research is to quantify and evaluate the effects of a climate controlled representation of Sr on the water fluxes modeled by the HTESSEL land surface model. Climate controlled Sr is here estimated with the memory method (MM) in which Sr is derived from the vegetation's memory of past root zone water storage deficits. Sr,MM is estimated for 15 river catchments over Australia across three contrasting climate regions: tropical, temperate and Mediterranean. Suitable representations of Sr,MM are implemented in an improved version of HTESSEL (MD) by accordingly modifying the soil depths to obtain a model Sr-MD that matches Sr,MM in the 15 catchments. In the control version of HTESSEL (CTR), Sr,CTR is larger than Sr,MM in 14 out of 15 catchments. Furthermore, the variability among the individual catchments of Sr,MM (117–722 mm) is considerably larger than of Sr,CTR (491–725 mm) The climate controlled representation of Sr in the MD version results in a significant and consistent improvement of the modeled monthly seasonal climatology (1975–2010) and inter-annual anomalies of river discharge compared with observations. However, the effects on biases in long-term annual mean fluxes are small and mixed. The modeled monthly seasonal climatology of the catchment discharge improved in MD compared to CTR: the correlation with observations increased significantly from 0.84 to 0.90 in tropical catchments, from 0.74 to 0.86 in temperate catchments and from 0.86 to 0.96 in Mediterranean catchments. Correspondingly, the correlations of the inter-annual discharge anomalies improve significantly in MD from 0.74 to 0.78 in tropical catchments, from 0.80 to 0.85 in temperate catchments and from 0.71 to 0.79 in Mediterranean catchments. The results indicate that the use of climate controlled Sr,MM can significantly improve the timing of modeled discharge and, by extension, also evaporation fluxes in land surface models. On the other hand, the method has not shown to significantly reduce long-term climatological model biases over the catchments considered for this study.

scholarly journals A Prototype Global Drought Information System Based on Multiple Land Surface Models

2014 ◽  
Vol 15 (4) ◽  
pp. 1661-1676 ◽  
Author(s):  
Bart Nijssen ◽  
Shraddhanand Shukla ◽  
Chiyu Lin ◽  
Huilin Gao ◽  
Tian Zhou ◽  
...  
Keyword(s):  
Real Time ◽  
Land Surface ◽  
Time Lag ◽  
Land Surface Models ◽  
Infiltration Capacity ◽  
Surface Moisture ◽  
Surface Models ◽  
Moisture Storage ◽  
Comparison Of The Results

The implementation of a multimodel drought monitoring system is described, which provides near-real-time estimates of surface moisture storage for the global land areas between 50°S and 50°N with a time lag of about 1 day. Near-real-time forcings are derived from satellite-based precipitation estimates and modeled air temperatures. The system distinguishes itself from other operational systems in that it uses multiple land surface models (Variable Infiltration Capacity, Noah, and Sacramento) to simulate surface moisture storage, which are then combined to derive a multimodel estimate of drought. A comparison of the results with other historic and current drought estimates demonstrates that near-real-time nowcasting of global drought conditions based on satellite and model forcings is entirely feasible. However, challenges remain because hydrological droughts are inherently defined in the context of a long-term climatology. Changes in observing platforms can be misinterpreted as droughts (or as excessively wet periods). This problem cannot simply be addressed through the addition of more observations or through the development of new observing platforms. Instead, it will require careful (re)construction of long-term records that are updated in near–real time in a consistent manner so that changes in surface meteorological forcings reflect actual conditions rather than changes in methods or sources.

scholarly journals Benchmarking global hydrological and land surface models against GRACE in a medium-size tropical basin

2021 ◽  
Author(s):  
Silvana Bolaños Chavarría ◽  
Micha Werner ◽  
Juan Fernando Salazar
Keyword(s):  
Water Resources ◽  
Land Surface ◽  
Water Storage ◽  
Land Surface Models ◽  
Global Models ◽  
Grace Data ◽  
Surface Models ◽  
Terrestrial Water ◽  
Long Term Trends

Abstract. The increasing reliance on global models to address climate and human stresses on hydrology and water resources underlines the necessity for assessing the reliability of these models. In river basins where availability of gauging information from terrestrial networks is poor, models are increasingly proving to be a powerful tool to support hydrological studies and water resources assessments. However, the lack of in-situ data hampers rigorous performance assessment, particularly in tropical basins where discordance between global models is considerable. Remotely sensed data of the terrestrial water storage obtained from the GRACE satellite mission can, however, provide independent data against which the performance of such global models can be evaluated. Here we assess the reliability of six global hydrological models (GHM) and four land surface models (LSM) available at two resolutions. We compare Total Water Storage (TWS)'s modelled dynamics with TWS derived from GRACE data over the Magdalena-Cauca basin in Colombia, a medium-sized tropical basin with a comparatively well-developed gauging network. We benchmark monthly TWS changes from each model against GRACE data for 2002–2014, evaluating monthly variability, seasonality, and long-term trends. TWS changes are evaluated at basin level, as well as for selected sub-basins with decreasing basin size. We find that the models poorly represent TWS for the monthly series, but they improve in representing seasonality and long-term trends. The high-resolution GHM W3RA model forced by the Multi-Source Weighted Ensemble Precipitation (MSWEP) is most consistent at providing the best performance at almost all basin scales, with higher-resolution models generally outperforming lower-resolution counterparts. This is, however, not the case for all models. Results highlight the importance of basin scale in the representation of TWS by the models, as with decreasing basin area, we note a commensurate decrease in the model performance. A marked reduction in performance is found for basins smaller than 60,000 km2. Although uncertainties in the GRACE measurement increase for smaller catchments, the models are clearly challenged in representing the complex hydrological processes of this tropical basin, as well as human influences. We conclude that GRACE provides a valuable dataset to benchmark global simulations of TWS change, in particular for those models with explicit representation of the internal dynamics of hydrological stocks, offering useful information for the continued improvement of large-scale hydrological and land-surface models of the global terrestrial water cycle, including in tropical basins.

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