Organizers: Natasha MacBean (1), Jana Kolassa (2), Andy Fox (2), Tristan Quaife (3), Hannah Liddy (4)
(1) Western University, Canada, (2) NASA GSFC, USA, (3) University of Reading, UK, (4) Columbia University/NASA GISS, USA
† Organized by the AIMES Land Data Assimilation Working Group

Workshop Overview

The 3rd annual Land Data Assimilation (DA) Community Virtual Workshop on “Recent Technical Developments in Land Data Assimilation” will took place on Tuesday to Wednesday 20th-21st June 9am-1pm ET / 15:00-19:00 CEST.

Technical challenges were the focus of this annual meeting as the scientific questions that lie behind those technical developments are typically the focus of other professional meetings and conferences. The goals of this workshop built on the goals of the AIMES Working Group on Land Data Assimilation to: 1) foster knowledge exchange across all groups working in land data assimilation; and 2) build a community of practice and collaboration in land DA, particularly for addressing the technical challenges we face in implementing land DA systems. To learn more about the outcomes of previous workshops, please check out the following: 

Learn more about the Land DA Community here: https://land-da-community.github.io.

Workshop Agenda

TUESDAY, 20 JUNE 2023

Using the 4DENVAR Data Assimilation Technique in Land Surface Models

Natalie Douglas(1*), Tristan Quaife(1)

(1)University of Reading. Whiteknights.

Abstract

Data Assimilation (DA) methods that combine well-known variational with ensemble techniques are emerging as new powerful tools that combat the pitfalls typically seen in variational DA experiments. The traditional 4DVar cost function sums a prior and an observational cost term each weighted by their uncertainty information. Problems commonly occur in the calculation of the background error covariance matrix (B-matrix) inverse and in the computation of the gradient function when minimising the cost function. The 4DEnVar technique avoids the complications of the original 4DVar cost function by incorporating terms calculated from a simple ensemble from state space. Firstly, a control variable transform to ensemble space in the form of an ensemble mean update eradicates the need for the inverse of the B matrix. Secondly, the simulated observations corresponding to the same ensemble can be used to approximate the terms in the gradient cost function which typically require the calculation of linearised observations operators and tangent linear models/adjoints. Consequently, the 4DEnVar technique requires only the use of full nonlinear observation operators and model runs and is an attractive method to adopt in terms of computational expense and simplicity. In addition to these benefits, an approximation to the posterior error covariance matrix can be obtained thus offering uncertainty information corresponding to optimised states. The technicalities of the 4DEnVar technique and some recent successes when applying it with the JULES land surface model will be summarised in this presentation.

Technical Aspects of the ECMWF Land Data Assimilation System

Ewan Pinnington(1*), Patricia de Rosnay(1)

(1)ECMWF, Shinfield Rd, Reading, UK

Abstract

In this talk we will cover some of the technical aspects of the current ECMWF Land Data Assimilation System (LDAS). This will include both the “offline” system used for seasonal forecast initialization and reanalysis experiments and the “operational” system used in the production of forecasts. We will highlight some developments being undertaken on the CERISE (CopERnIcus climate change Service Evolution) project to move towards coupled atmosphere-land data assimilation and increase the utilization of ensemble information within the system. The current system at ECMWF is a Simplified Extended Kalman Filter (SEKF). However, the system does incorporate information from the ECMWF atmospheric Ensemble of Data Assimilations (EDA) to diagnose the Jacobian of the observations operator. We will show how use of the EDA within the specification of prior spread in the SEKF has improved forecast skill. The talk will also highlight other technical developments for the parallelization of the “offline” system using the Dask Python package, drawing inspiration from the Pangeo community.

Terrestrial Carbon Community Assimilation System

Thomas Kaminski(1*), Wolfgang Knorr, Michael Voßbeck, Mathew Williams, Timothy Green, Luke Smallman, Marko Scholze, Tristan Quaife, Tea Thum, Sönke Zaehle, Peter Rayner, Susan Steele-Dunne, Mariette Vreugdenhil, Tuula Aalto, Mika Aurela, Alexandre Bouvet, Emanuel Bueechi, Wouter Dorigo, Tarek S. El-Madany, Tiana Hammer, Marika Honkanen, Derek Houtz, Francois Jonard, Yann H. Kerr, Anna Kontu, Juha Lemmetyinen, Hannakaisa Lindqvist, Arnaud Mialon, Amanda Ojasalo, Gaetan Pique, Shaun Quegan, Pablo Reyez Muñoz, Nemesio Rodriguez-Fernandez, Mike Schwank, Jochem Verrelst, Matthias Drusch, and Dirk Schüttemeyer

(1) The Inversion Lab, Martinistr. 21, 20251 Hamburg, Germany

Abstract

We report on the development of the Terrestrial Carbon Community Assimilation System (TCCAS), an activity funded by the European Space Agency within its Carbon Science Cluster. TCCAS is built around the newly developed DALEC&BETHY (D&B) terrestrial biosphere model. D&B builds on the strengths of each component model in that it combines the dynamic simulation of the carbon pools and canopy phenology of DALEC with the dynamic simulation of water pools, and the canopy model of photosynthesis and energy balance of BETHY. A suite of observation operators allows the simulation of solar-induced fluorescence, fraction of absorbed photosynthetically active radiation, vegetation optical depth from passive microwave sensors, the slope of the backscatter-incidence angle relationship of an active microwave sensor, and surface layer soil moisture. The model is embedded into a variational assimilation system that adjusts a combination of initial pool sizes and process parameters to match the observational data streams. For this purpose TCCAS is provided with efficient tangent and adjoint code. TCCAS will be released as a community tool. We will present the system and show applications over study regions in Finland and Spain.

Calibrating for Biodiversity Impact in a Crop Model

Toni Viskari(1*), Quentin Bell(1), Tristan Quaife(2), Istem Bell(1), and Jari Liski(1)

(1) Finnish Meteorological Institute, Erik Palmenin Aukio 1, 00560 Helsinki, Finland
(2) University of Reading, Reading, United Kingdom

Abstract

To improve agricultural soil health and carbon allocation, there has been an increased push to increase the use of cover crops in cash crop cultivation. These efforts cause challenges for agricultural system modeling, though, as many of the relevant biodiversity processes are not currently included in ecosystem models. Implemented on a test farm site in Helsinki, Finland from 2018 to 2023, the TWINWIN project examines how different combinations of cover crops grown with Barley affect both the productivity as well as the various soil conditions of the plot. The modeling component of this project has been attempting to sidestep the biodiversity model inclusion challenge by instead trying to calibrate specific Barley parameters to mirror how the plots are affected by different cover crops. While the parameter values estimated with this approach might not be the actual ones, they should allow us to better model how the mixed crops perform in different environments and climate conditions. For calibration we used the 4-Dimensional Variational Assimilation (4DEnVar) algorithm provided by Reading University research. As the crop model, we used the Stics crop model developed for both annual and perennial crops. The preliminary results have shown promise in capturing certain trends especially concerning yield, but they have also illustrated how important it is to consider both what the measurements and model variables actually represent when choosing observations to use for calibration.

Contrasting Flash Droughts Captured by Soil Moisture and Vegetation Data Assimilation

Shahryar K. Ahmad(1,2*), Sujay V. Kumar(1), Timothy M. Lahmers(1,3)

(1)Hydrological Sciences Lab, NASA Goddard Space Flight Center (NASA GSFC), Greenbelt, MD.
(2)Science Applications International Corporation, McLean, VA, USA
(3)Earth System Science Interdisciplinary Center (ESSIC), University of Maryland, College Park, MD, USA

Abstract

Droughts form one end of the hydrologic extremes that usually evolve over months to years to reach their peak intensity. However, another category termed as flash droughts evolve and intensify very rapidly under the influence of extreme atmospheric conditions. Such events can have multiple triggering factors, including those driven by precipitation deficits or extreme heat, often leading to distinct mechanisms of progression. The northern Great Plains of the United States recently experienced two such flash droughts in 2016 and 2017. In 2016, an early heat wave during March caused warmer-than-normal conditions, leading to increased evaporative demands. The 2017 drought experienced near-record-low precipitation anomalies causing rapid depletion of soil moisture. Persistent dry soil conditions led to increased vegetation stress, severely impacting crop yield. Using the NASA Land Information System (LIS) framework, we demonstrate that data assimilation (DA) of various remote sensing observations within Noah-Multiparameterization (Noah-MP) model is essential in capturing the progression of these two contrasting flash droughts. Results suggest that during the 2016 drought, characterized by an intense heat wave, assimilation of MODIS-derived leaf area index (LAI) within Noah-MP helped the model to capture elevated transpiration at drought onset followed by declining soil moisture. LAI-assimilated soil moisture anomalies exhibit increases of 7.5% and 7.1% in similarity with Evaporative Stress Index data and U.S. Drought Monitor maps, respectively. However, for the precipitation-deficit-driven drought of 2017, assimilating SMAP soil moisture helped capture the rapid drought intensification and resulted in 6.7% and 8.8% higher similarity with respective datasets. The value in assimilating different variables in the two cases can be attributed to the distinct impacts that the precipitation deficit and heat wave had on the vegetation. The two case studies highlight the overarching need of multivariate DA using remote sensing observations to comprehensively capture the different processes controlling the progression of flash droughts.

Coupled surface hydrology and data assimilation: Applications and technical implementation of the coupled LIS/WRF-Hydro system

Timothy M. Lahmers(1,2*), Sujay V. Kumar(1), Daniel Rosen(3), Aubrey Dugger(4), David J. Gochis(4), Joseph A. Santanello(1), Chandana Gangodagamage(1,2), and Rocky Dunlap(3)

(1) Hydrological Sciences Lab, NASA Goddard Space Flight Center (NASA-GSFC), Greenbelt, MD, USA.
(2) Earth System Science Interdisciplinary Center (ESSIC), University of Maryland, College Park, MD, USA.
(3) NOAA/Earth System Research Laboratory, Boulder, CO, USA.
(4) National Center for Atmospheric Research, Boulder, Colorado, USA.

Abstract

The NASA LIS/WRF-Hydro system is a coupled modeling framework that combines the modeling and data assimilation (DA) capabilities of the NASA Land Information System (LIS) with the multi-scale surface hydrological modeling capabilities of the WRF-Hydro model, both of which are widely used in both operations and research. This coupled modeling framework builds on the linkage between land surface models (LSMs), which simulate surface boundary conditions in atmospheric models, and distributed hydrologic models, which simulate horizontal surface and sub-surface flow, adding new land DA capabilities. In the present study, we employ this modeling framework in the Tuolumne River basin in central California. We demonstrate the added value of the assimilation of NASA Airborne Snow Observatory (ASO) snow water equivalent (SWE) estimates in the Tuolumne basin. This analysis is performed in both LIS as an LSM column model and LIS/WRF-Hydro, with hydrologic routing. Results demonstrate that ASO DA in the basin reduced snow bias by as much as 30% from an open-loop (OL) simulation compared to three independent datasets. It also reduces downstream streamflow runoff biases by as much as 40%, and improves streamflow skill scores in both wet and dry years. These results have potential applications for water management and understanding hydrologic extremes. In this presentation, we explore the technical configuration of the LIS/WRF-Hydro system and how we leverage ESMF National Unified Operational Prediction Capability (NUOPC) features to couple the LIS and WRF-Hydro systems. Ongoing work with this system, including applications to study water temperature and improve streamflow drought prediction, are also discussed.

Systematic Errors in Simulated L-Band Brightness Temperature in the SMAP Level-4 Soil Moisture Analysis

Rolf Reichle(1*), Qing Liu(1), Michel Bechtold(2), Wade Crow(3), Gabrielle De Lannoy(2), Andrew Fox(1), John Kimball(4), and Randal Koster(1)

(1) Global Modeling and Assimilation Office, NASA/GSFC, Greenbelt, MD, USA.
(2) KULeuven, Leuven, Belgium.
(3) Hydrology and Remote Sensing Laboratory, USDA/ARS, Beltsville, MD, USA.
(4) University of Montana, Missoula, MT, USA.

Abstract

The NASA Soil Moisture Active Passive (SMAP) mission has been providing L-band (1.4 GHz) brightness temperature (Tb) observations since April 2015. By assimilating the Tb observations into the NASA Catchment land surface model using a spatially distributed ensemble Kalman filter (EnKF), the SMAP Level-4 Soil Moisture (L4_SM) product provides global, 3-hourly, 9-km resolution estimates of surface (0-5 cm) and root-zone (0-100 cm) soil moisture with ~2.5-day latency for use in research and Applications.

The EnKF-based L4_SM analysis assumes unbiased forecast errors. Consequently, the seasonally varying bias between the model forecast Tb and the observed values is removed prior to the assimilation of the SMAP Tb observations. The L4_SM system is thus designed to only correct errors in synoptic-scale and interannual variations from the long-term mean seasonal cycle while maintaining the model’s (possibly wrong) Climatology.

In this paper, we examine the Tb observation-minus-forecast (O-F) residuals from the L4_SM Version 7 product (computed after rescaling the Tb observations to the mean seasonal cycle of the simulated Tb). The long-term average of the Tb O-F residuals has a global mean of only 0.13 K and locally small values, ranging from -1 to 3 K. The model forecast Tb, however, still exhibits undesirable systematic errors relative to the (rescaled) Tb observations. At some locations, the time-average Tb O-F values strongly depend on surface soil moisture (SM). At the Yanco SMAP core validation site, for example, the Tb O-F residuals typically range from 5 to 15 K under dry soil moisture conditions (SM < 0.15 m 3 m -3 ) yet are predominantly negative under wet soil moisture conditions (SM > 0.25 m 3 m -3 ), with values ranging from 0 to -40 K. This results in soil moisture analysis increments that persistently make the soil drier under dry SM conditions and persistently make the soil wetter under wet SM conditions, suggesting an error in the dynamic range of the simulated Tb, soil moisture or soil temperature.

In this paper, we describe the higher-order systematic Tb forecast errors in more detail, examine their impact on the L4_SM product quality, and explore potential avenues to improve the L4_SM algorithm.

WEDNESDAY, 21 JUNE 2023

Ecological memory of net ecosystem exchange

Yao Liu (1*)

(1) Northumbria University, Newcastle upon Tyne, NE1 8ST, United Kingdom

Abstract

Land-carbon dynamics are determined by not only current conditions, but also past conditions that no longer persist. Quantifying such memory effects is therefore crucial for understanding and predicting the carbon cycle. In this talk, a framework for quantifying environmental and biological memory (Ogle et al., 2015) is introduced. Then, using data from 42 eddy covariance sites across six major biomes, a set of Bayesian statistical models were implemented to quantify the strength, temporal feature, and primary contributors of memory in daily net carbon exchange (NEE; Liu et al., 2019). Memory is important for explaining the land-carbon metabolism, especially in drylands for which it explains approximately 32% of the variation in NEE. The strong environmental memory in drylands was driven by both short- and long- term moisture status. Contrary to common belief, sites of the same biome-type do not exhibit similar environmental sensitivity and memory in their NEE responses. Instead, the strength of environmental memory scales with increasing water stress both within and among major biomes (Fig. 1a), suggesting a potential adaptive response to water limitation. These findings highlight the necessity of considering ecological memory in experiment, observation, and modelling. Finally, I discuss limitations of this approach to memory quantification and introduce some future directions, including causal-state reconstruction (i.e., representing NEE dynamics as internal states of the same causal information evolving through time; Fig. 1b) and identifying anticipatory memory in fluctuating environments

Spatial Heterogeneity of Methane Emissions from Peatlands in the Northern Hemisphere

Elodie Salmon(1*) , Fabrice Jégou(2) , Bertrand Guenet(3) , Line Jourdain(2) , Chunjing Qiu(1) , Vladislav Bastrikov(4) , Christophe Guimbaud(2) , Dan Zhu(5) , Philippe Ciais(1), Philippe Peylin(1) , Sébastien Gogo(6), Fatima Laggoun-Défarge(7)

(1)Laboratoire des Sciences du Climat et de l’Environnement, UMR8212, CEA-CNRS-UVSQ F-91191 Gif sur Yvette, France.
(2) Laboratoire de Physique et Chimie de l’Environnement et de l’Espace, LPC2E, UMR 7328, Université d’Orléans, CNRS, CNES, 45071, Orléans cedex 2, France.
(3) Laboratoire de Géologie de l’ENS, IPSL, CNRS, PSL Research University, 24 rue Lhomond, 75231 Paris cedex 05, France.
(4) Science Partners, 75010 Paris, France.
(5) Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, China.
(6) ECOBIO (Écosystèmes, Biodiversité, Évolution), Université Rennes 1, CNRS UMR 6553, Rennes, France
(7) Institut des Sciences de la Terre d’Orléans, Université d’Orléans, CNRS, BRGM, UMR 7327, 45071 Orléans, France.

Abstract

Land-carbon dynamics are determined by not only current conditions, but also past conditions that no longer persist. Quantifying such memory effects is therefore crucial for understanding and predicting the carbon cycle. In this talk, a framework for quantifying environmental and biological memory (Ogle et al., 2015) is introduced. Then, using data from 42 eddy covariance sites across six major biomes, a set of Bayesian statistical models were implemented to quantify the strength, temporal feature, and primary contributors of memory in daily net carbon exchange (NEE; Liu et al., 2019). Memory is important for explaining the land-carbon metabolism, especially in drylands for which it explains approximately 32% of the variation in NEE. The strong environmental memory in drylands was driven by both short- and long- term moisture status. Contrary to common belief, sites of the same biome-type do not exhibit similar environmental sensitivity and memory in their NEE responses. Instead, the strength of environmental memory scales with increasing water stress both within and among major biomes (Fig. 1a), suggesting a potential adaptive response to water limitation. These findings highlight the necessity of considering ecological memory in experiment, observation, and modelling. Finally, I discuss limitations of this approach to memory quantification and introduce some future directions, including causal-state reconstruction (i.e., representing NEE dynamics as internal states of the same causal information evolving through time; Fig. 1b) and identifying anticipatory memory in fluctuating environments

Convergence in simulating global soil organic carbon by structurally different models after data-model fusion

Feng Tao(1*) and Yiqi Luo(2)

(1) Department of Earth System Science, Tsinghua University, Beijing, China
(2) School of Integrative Plant Science, Cornell University, Ithaca, NY, USA

Abstract

Soils store the largest organic carbon in the terrestrial biosphere, yet representations of soil organic carbon (SOC) present huge uncertainty by different biogeochemical models. Model structure has been identified as a major contributor to inter-model uncertainties in simulating SOC by biogeochemical models. While biogeochemical models with different structures and ad hoc parameterizations simulate diverging spatial patterns of SOC across the globe, here we show that different models simulate converged SOC storage and its related model components after being constrained by the same observational data. We applied the PROcess-guided deep learning and DAta-driven modeling (PRODA) approach to simultaneously inform a linear model (i.e., Community Land Model version 5, CLM5) that features first-order kinetics and a non-linear microbial model that characterizes Michaelis-Menten kinetics in SOC decomposition with the same soil database containing >50,000 global distributed SOC vertical profiles. Two models after being optimized by the PRODA approach agree with each other well on simulating global SOC storage and its spatial patterns. Observational SOC data effectively constrains key model components such as carbon transfer efficiency, baseline decomposition rate, and environmental modification and thus eventually contributes to similar SOC patterns by structurally different biogeochemical models. Moreover, the Michaelis constant in the microbial model after being informed by SOC observations shows to be much larger than its corresponding substrate concentration in SOC decomposition. Thus, the nonlinear microbial model can be well approximated by a simplified first-order structure in simulating SOC at the global scale without sacrificing explanatory power. Our results highlight the importance of fusing observational data with structurally-different biogeochemical models to gain converged representations of the soil carbon cycle and identify the most probable model structure at investigated scale.

Constraining Plant Water Dynamics in Land Surface Model through the Assimilating ASCAT Normalized Backscatter and Slope at ISMN Stations over Western Europe

Xu Shan(1*), Susan Steele-Dunne(1), Sebastian Hahn(2), Wolfgang Wagner(2), Bertrand Bonan(3), Clement Albergel(3,4), Jean-Christophe Calvet(3), Ou Ku(5)

(1) Department of Geoscience and Remote Sensing, Faculty of Civil Engineering and Geosciences, TU Delft, Delft, the Netherlands. 
(2) Department of Geodesy and Geoinformation (GEO), Vienna University of Technology, Vienna, Austria.
(3) CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France.
(4) now at European Space Agency Climate Office, ECSAT, Harwell Campus, Didcot, Oxfordshire, UK.
(5) Netherlands eScience Center, Amsterdam, the Netherlands.

Abstract

Previous studies demonstrated ASCAT backscatter and slope contain valuable information about plant water dynamics (Steele-Dunne et al., 2019). In this study, ASCAT normalized backscatter and slope are jointly assimilating into the ISBA-A-gs land surface model (LSM) to constrain plant water dynamics processes. A Deep Neural Network (DNN) is trained following approaches in Shan et al., 2022, as an observation operator embedded in an Extended Kalman Filter (EKF). Data Assimilation (DA) and model open loop (OL) experiments are ran on ASCAT grid points (GPIs) containing ISMN stations in Western Europe and validated using data from 2017 to 2019. Performances of DA and OL are evaluated against ISMN in-situ soil moisture observations at different soil depths and satellite-based LAI from the 1 km v2 Copernicus Global Land Service project (CGLS).

Overall DA shows neutral improvements in domain median values when considering unbiased Root Mean Square Error compared to OL against the observations. However, improvement is observed at specific times of year. For example, analysis of the monthly performances in Agricultural GPIs shows that DA corrects deeper soil moisture in spring. This echoes our previous studies which demonstrated an indirect link between deeper soil water availability and vegetation water status revealed by ASCAT slope. Potential reasons for worse performances at other times are also analyzed. Validation performances of DNN might have an effect on DA performances. Other reasons include that OL reaches already satisfactory ubRMSE (<0.05 m3m-3) comparing to ISMN observations. In addition, it is important to note that DA is performed at the spatial resolution of ASCAT (25 km), while the ISMN provides point-scale information. Analysis of DA performance statistics future efforts can be done to improve the robustness of DNN.

Is a steady-state relaxation parameter a viable solution to the spin-up problem in land data assimilation?

Nina Raoult(1*), Natasha MacBean(2), Cedric Bacour(3), Philippe Peylin(3), Vladislav Bastrikov(4)

(1)Department of Mathematics and Statistics, University of Exeter, U.K. 
(2)Departments of Geography & Environment and Biology, Western University, London, Ontario, Canada
(3) Laboratoire des Sciences du Climat et de l’Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, Gif-sur-Yvette, 91191, France
(4) Science Partners, France

Abstract

One of the challenges in land data assimilation is accounting for the carbon stock trajectories. To initialise land surface models, a two-step spin-up is often performed. The first step helps to put the prognostic variables, including vegetation station, soil carbon pools, and soil moisture at equilibrium under pre-industrial CO2 conditions. The second step is a transient run which allows for simulations to start with the correct atmospheric conditions. However, since these runs are computationally costly, they are often neglected when performing model calibrations. Instead, it is common to rely on a steady-state relaxation parameter to correct the magnitude of the initial soil carbon content during the optimisations. This presentation will discuss the challenges encountered when using such a parameter based on our experiences with the ORCHIDEE land surface model. One challenge concerns the interpretability of such a parameter when used at a site or regional scale. Another is how adding the nitrogen cycle to ORCHIDEE impacts the effectiveness of this parameter. Finally, we will discuss future avenues and whether we are in a position to remove this parameter from our carbon optimisations with the rise of emulators.

Optimizing Maximum Carboxylation Rate for North America’s Boreal Forests in a Terrestrial Biosphere Model

Bo Qu (1,2,3*), Alexandre Roy (2,3), Joe R. Melton (4) , Jennifer L. Baltzer (5), Youngryel Ryu (6), Matteo Detto (7), Oliver Sonnentag (1,2)

(1) Département de géographie, Université de Montréal, Montréal, Canada
(2) Centre d’Études Nordiques, Québec, Canada
(3) Centre de recherche sur les interactions bassins versants-écosystèmes aquatiques (RIVE), Université du Québec à Trois-Rivières, Trois-Rivières, Canada
(4) Climate Research Division, Environment and Climate Change Canada, Victoria, Canada
(5) Biology Department, Wilfrid Laurier University, Waterloo, Canada
(6) Department of Landscape Architecture and Rural Systems Engineering, College of Agriculture and Life Sciences, Seoul National University, Seoul, Republic of Korea
(7) Department of Ecology and Evolutionary Biology, Princeton University, Princeton, New Jersey, USA

Abstract

The maximum carboxylation rate (Vcmax) is an important parameter for the coupled simulation of gross primary production (GPP) and evapotranspiration (ET) in terrestrial biosphere models such as the Canadian Land Surface Scheme Including Biogeochemical Cycles (CLASSIC). Vcmax is often parameterized for plant functional types (PFTs). Assuming that Vcmax is constant in time and space introduces large uncertainties in simulated GPP and ET. Using eddy covariance observations made at eight mature boreal forest stands of North America, we optimized Vcmax25 (Vcmax at 25 °C) for six tree, shrub, and herb PFTs in CLASSIC with a Bayesian algorithm. Results showed substantial reduction in root mean square deviation for GPP and ET at almost all and two permafrost-free sites, respectively, when comparing the optimized simulations with several corresponding gridded estimates. The optimized PFT-Vcmax25 compared well with reported estimates from field observations. Remarkable Vcmax25 variations were identified among sites for shrub and herb PFTs. Variations in PFT-Vcmax25 closely associated with site conditions in latitude, air temperature and start and end of growing seasons.

Designing emulation tasks that preserve physical relationships for land model calibration

Linnia Hawkins (1), Daniel Kennedy (2), Katie Dagon (2), Pierre Gentine (1), Dave Lawrence (2)

(1)Earth and Environmental Engineering, Columbia University. New York, NY, USA
(2) Climate and Global Dynamics Laboratory, National Center for Atmospheric Research, Boulder, CO, USA

Abstract

Land surface model parameter calibration efforts typically utilize a surrogate model or emulator for parameter optimization due to the computational expense of simulations. Emulators generally are trained to represent the relationship between the high dimensional parameter inputs and some measure of model error. Defining the error that parameter calibration exercises aim to minimize is a critical task in the optimization process. Often, the error is a combination of multiple variables merged over space and time (referred to hereafter as ‘total error’). Building a robust emulator that accurately represents the relationship between input parameters and the total error can be a challenging task and often results in discrepancies between the emulated and modeled total error. This is partially due to the degradation of the physical relationships between parameter settings and model processes when emulating the total error. Here we experiment with an alternative approach designed to simplify the emulation task and preserve physical relationships between input parameters and modeled processes. We train a suite of emulators to predict specific model output variables for particular biomes. We then calculate the total error using the emulated values and identify model parameterizations that minimize error. We performed this experiment in the Community Land Model and compared simulations using the resulting ‘optimal’ model parameterizations identified using the two approaches. This comparison demonstrates the importance of experimental design choices made throughout the parameter calibration process.