Carbon, water and energy exchange between the land and atmosphere controls how ecosystems either accelerate or ameliorate the effect of climate change. However, evaluating improvements to processes controlling carbon cycling, water use and energy exchange in global land surface models (LSMs) remains challenging in part because of persistent model errors in estimating leaf area. Here we evaluate the changes in global carbon, water and energy exchange brought about when a LSM prognostic estimates of leaf area are made consistent with estimates from satellites. This approach achieves two aims; first to quantify the effect of ignoring errors in leaf area index (LAI) on land‐atmosphere fluxes and second, to evaluate how closely this LSM replicates fluxes with and without an LAI constraint. We implemented an ensemble Kalman filter with spatiotemporal adaptive inflation to more closely match community land model (CLM5.0) estimates of leaf area to those from the Global Inventory Modeling and Mapping Studies leaf area index (LAI3g) product. We then evaluate the model's estimates of gross primary productivity (GPP) and latent heat flux (LE) against well established global estimates of these fluxes. We find that the model is biased high by 27% relative to the LAI3g product. Moreover, the effect of bias in LAI is substantial for GPP (18%) and LE (6%) and likely to confound efforts to refine processes controlling these fluxes. This data assimilation approach serves as a method to evaluate the efficacy of refinements to flux processes until the processes controlling the dynamics of LAI are better resolved in LSMs.

An ensemble Kalman filter reanalysis has been archived in the Research Data Archive at the National Center for Atmospheric Research. It used a CAM6 configuration of the Community Earth System Model (CESM), several million observations per day, and the Data Assimilation Research Testbed (DART). The data saved from this global, [Formula: see text] resolution, 80 member ensemble span 2011-2019. They include ensembles of: sub-daily, real world, atmospheric forcing for use by all of the nonatmospheric models of CESM; weekly, CAM6, restart file sets; 6 hourly, prior hindcast estimates of the assimilated observations; 6 hourly, land model, plant growth variables, and 6 hourly, ensemble mean, gridded, atmospheric analyses. This data can be used for hindcast studies and data assimilation using component models of CESM; CAM6, CLM5, CICE5, POP2. MOM6, MOSART, and CISM; and non-CESM Earth system models. This large dataset (~ 120 Tb) has a unique combination of a large ensemble, high frequency, and multiyear time span, which provides opportunities for robust statistical analysis and use as a machine learning training dataset.

Abstract. Predicting major floods during extreme rainfall events remains an important challenge. Rapid changes in flows over short time-scales combined with multiple sources of model error makes it difficult to accurately simulate intense floods. This study presents a general data assimilation framework that aims to improve flood predictions in channel routing models. Hurricane Florence, which caused catastrophic flooding and damages in the Carolinas in September 2018, is used as a case study. The National Water Model (NWM) configuration of the WRF-Hydro modeling framework is interfaced with the Data Assimilation Research Testbed (DART) to produce ensemble streamflow forecasts and analyses. Hourly streamflow observations from 107 United States Geological Survey (USGS) gauges are assimilated for a period of one month. The data assimilation (DA) system developed in this paper explores two novel contributions: (1) Along-The-Stream (ATS) covariance localization and (2) spatially and temporally varying adaptive covariance inflation. ATS localization aims to mitigate not only spurious correlations, due to limited ensemble size, but also physically incorrect correlations between unconnected and indirectly connected state variables in the river network. We demonstrate that ATS localization provides improved information propagation during the model update. Adaptive prior inflation is used to tackle errors in the prior, including large model biases. Analysis errors incurred during the update are addressed using posterior inflation. Results show that ATS localization is a crucial ingredient of our hydrologic DA system, providing at least 40% more accurate (RMSE) streamflow estimates than regular, Euclidean distance-based localization. Assessment of hydrographs indicates that adaptive inflation is extremely useful and perhaps indispensable for improving the forecast skill during flooding events with significant model errors. We argue that adaptive prior inflation is able to serve as a vigorous bias correction scheme which varies both spatially and temporally. Major improvements over the model's severely underestimated streamflow estimates are suggested along Pee Dee River in South Carolina and many other locations in the domain, where inflation is able to avoid filter divergence and thereby assimilate significantly more observations.

Uncertain or inaccurate parameters in sea ice models influence seasonal predictions and climate change projections in terms of both mean and trend. We explore the feasibility and benefits of applying an ensemble Kalman filter (EnKF) to estimate parameters in the Los Alamos sea ice model (CICE). Parameter estimation (PE) is applied to the highly influential dry snow grain radius and combined with state estimation in a series of perfect model observing system simulation experiments (OSSEs). Allowing the parameter to vary in space improves performance along the sea ice edge but degrades in the central Arctic compared to requiring the parameter to be uniform everywhere, suggesting that spatially varying parameters will likely improve PE performance at local scales and should be considered with caution. We compare experiments with both PE and state estimation to experiments with only the latter and have found that the benefits of PE mostly occur after the data assimilation period, when no observations are available to assimilate (i.e., the forecast period), which suggests PE's relevance for improving seasonal predictions of Arctic sea ice.

Predicting major floods during extreme rainfall events remains an important challenge. Rapid changes in flows over short timescales, combined with multiple sources of model error, makes it difficult to accurately simulate intense floods. This study presents a general data assimilation framework that aims to improve flood predictions in channel routing models. Hurricane Florence, which caused catastrophic flooding and damages in the Carolinas in September 2018, is used as a case study. The National Water Model (NWM) configuration of the WRF-Hydro modeling framework is interfaced with the Data Assimilation Research Testbed (DART) to produce ensemble streamflow forecasts and analyses. Instantaneous streamflow observations from 107 United States Geological Survey (USGS) gauges are assimilated for a period of 1 month. The data assimilation (DA) system developed in this paper explores two novel contributions, namely (1) along-the-stream (ATS) covariance localization and (2) spatially and temporally varying adaptive covariance inflation. ATS localization aims to mitigate not only spurious correlations, due to limited ensemble size, but also physically incorrect correlations between unconnected and indirectly connected state variables in the river network. We demonstrate that ATS localization provides improved information propagation during the model update. Adaptive prior inflation is used to tackle errors in the prior, including large model biases which often occur in flooding situations. Analysis errors incurred during the update are addressed using posterior inflation. Results show that ATS localization is a crucial ingredient of our hydrologic DA system, providing at least 40 % more accurate (root mean square error) streamflow estimates than regular, Euclidean distance-based localization. An assessment of hydrographs indicates that adaptive inflation is extremely useful and perhaps indispensable for improving the forecast skill during flooding events with significant model errors. We argue that adaptive prior inflation is able to serve as a vigorous bias correction scheme which varies both spatially and temporally. Major improvements over the model's severely underestimated streamflow estimates are suggested along the Pee Dee River in South Carolina, and many other locations in the domain, where inflation is able to avoid filter divergence and, thereby, assimilate significantly more observations.

The Western United States is dominated by natural lands that play a critical role for carbon balance, water quality, and timber reserves. This region is also particularly vulnerable to forest mortality from drought, insect attack, and wildfires, thus requiring constant monitoring to assess ecosystem health. Carbon monitoring techniques are challenged by the complex mountainous terrain, thus there is an opportunity for data assimilation systems that combine land surface models and satellite-derived observations to provide improved carbon monitoring. Here, we use the Data Assimilation Research Testbed to adjust the Community Land Model (CLM5.0) with remotely sensed observations of leaf area and above-ground biomass. The adjusted simulation significantly reduced the above-ground biomass and leaf area, leading to a reduction in both photosynthesis and respiration fluxes. The reduction in the carbon fluxes mostly offset, thus both the adjusted and free simulation projected a weak carbon sink to the land. This result differed from a separate observation-constrained model (FLUXCOM) that projected strong carbon uptake to the land. Simulation diagnostics suggested water limitation had an important influence upon the magnitude and spatial pattern of carbon uptake through photosynthesis. We recommend that additional observations important for water cycling (e.g., snow water equivalent, land surface temperature) be included to improve the veracity of the spatial pattern in carbon uptake. Furthermore, the assimilation system should be enhanced to maximize the number of the simulated state variables that are adjusted, especially those related to the recommended observed quantities including water cycling and soil carbon.

The performance of two common approaches to data assimilation, an Ensemble Adjustment Kalman Filter (EAKF) and a 4-dimensional variational (4D-Var) method, is quantified in a popular community ocean model, the Regional Ocean Modeling Systems (ROMS). Two distinct circulation environments are considered: the California Current System (CCS), which is an eastern boundary upwelling regime, and the Indian Ocean (IO) characterized by an equatorial waveguide subject to the energetic seasonal reversals of the Indian and Asian Monsoons. In the case of the CCS, experiments were performed using synthetic observations, so-called Observing System Simulation Experiments (OSSEs). An extensive suite of CCS OSSEs were conducted to explore the performance of both data assimilation approaches to system configuration. For the EAKF, this includes the method for generating the seed ensemble, ensemble size, localization scales, and the length of the assimilation window. In the case of 4DVar, the influence of assimilation window length, and the formulation of the background error covariance were explored. The performance of the EAKF was found to be influenced most by the size of the ensemble and by the method used to generate the initial seed ensemble where centering of the ensemble was found to yield improvement. For 4D-Var, the assimilation window length is by far the most critical factor, with an increase in system performance as the window length is extended. In general, the EAKF and 4D-Var systems converge to similar solutions over time, which are independent of the starting point. The EAKF employs a First-Guess at Appropriate Time (FGAT) strategy, and some experiments indicate that short FGAT windows can be problematic due to the introduction of frequent initialization shocks. While the EAKF generally out-performs 4D-Var in the OSSEs, analysis of the innovations from the two systems through time indicates that they track each other closely. Additional Observing System Experiments (OSEs) were performed in the CCS and IO configurations of ROMS using real ocean observations. In this case, the comparison of the EAKF and 4D-Var state estimates with independent observations indicates that the EAKF and 4D-Var state estimates diverge over time, although the 4D-Var estimates are somewhat better by some measures. The relative performance of the EAKF and 4D-Var systems is similar across the wide-range of circulation regimes that characterize the CCS and IO, suggesting that the results presented here are a robust indicator of expected performance in other regions of the world ocean.

The National Center for Atmospheric Research (NCAR) has recently released version 2.1 of the Community Earth System Model (CESM 2.1). A twenty-year, 80-member ensemble atmospheric reanalysis with 1-degree resolution in the CAM6 atmospheric model is being produced using NCAR’s Data Assimilation Research Testbed (DART) to support a variety of climate research goals. A standard configuration of CAM and the CLM5 land surface model will be coupled to a prescribed ocean and sea ice. Eventually, the reanalyisis will generate a final product that extends from 1999 to the present. Observations being assimilated include in situ observations used in the operational NCEP CFSR reanalysis along with GPS occultation observations and remote sensing temperature retrievals. The primary goal is to provide an ensemble of atmospheric forcing that can be used to generate additional ensemble reanalyses for other components of CESM including CLM, the POP and MOM6 ocean models, and the CICE sea ice model. Highlights of results from the first 10-years of the reanalysis will be presented. Results will include evaluation of short-term forecasts in observation space for root mean square error, ensemble spread, and ensemble consistency. In addition, key aspects of the atmospheric forcing files for other components of the climate system will be discussed.

The Data Assimilation Research Testbed (DART) is a community facility for ensemble data assimilation developed and maintained by the National Center for Atmospheric Research (NCAR). DART provides ensemble data assimilation capabilities for NCAR community earth system models and many other prediction models. It is straightforward to add interfaces for new models and new observations to DART. DART provides traditional ensemble data assimilation algorithms that implicitly assume Gaussianity and linearity. Traditional algorithms can still work when these assumptions are violated. However, it is possible to greatly improve results by extending ensemble algorithms to explicitly account for aspects of nonlinearity and non-Gaussianity. Two new algorithms have been added to DART. 1). Anamorphosis transforms variables to make the assimilation problem more linear and Gaussian before transforming posterior estimates back to the original model variables; 2). The marginal correction rank histogram filter (MCRHF) directly represents arbitrary non-Gaussian distributions. These methods are particularly valuable for data assimilation for bounded quantities like tracers or streamflow. DART is being applied to a number of novel applications. Examples in the poster include 1). An eddy-resolving global ocean ensemble reanalysis with the POP ocean model and an ensemble optimal interpolation; 2). The WRF-Hydro/DART system now includes a multi-parametric ensemble, anamorphosis, and spatially-correlated noise for the forcing fields. 3). Results from the Carbon Monitoring System over Mountains using CLM5 to assimilate remotely-sensed observations (LAI, biomass, and SIF) for a field site in Colorado; 4). Assimilation of MODIS snow cover fraction and daily GRACE total water storage data and its impact on soil moisture using the DART/NOAH-MP system. 5). An ensemble atmospheric reanalysis using the CAM general circulation model.

Abstract An ensemble optimal interpolation (EnOI) data assimilation system for a high‐resolution (0.1° horizontal) version of the Community Earth System Model Version 2 (CESM2) ocean component is presented. For this purpose, a new version of the Data Assimilation Research Testbed (DART Manhattan) that enables large‐state data assimilation by distributing state vector information across multiple processors at high resolution is used. The EnOI scheme uses a static (but seasonally varying) 84‐member ensemble of precomputed perturbations to approximate samples from the forecast error covariance and utilizes a single model integration to estimate the forecast mean. Satellite altimetry and sea surface temperature observations along with in situ temperature and salinity observations are assimilated. This new data assimilation framework is then used to produce a global high‐resolution retrospective analysis for the 2005–2016 period. Not surprisingly, the assimilation is shown to generally improve the time‐mean ocean state estimate relative to an identically forced ocean model simulation where no observations are ingested. However, diminished improvements are found in undersampled regions. Lack of adequate salinity observations in the upper ocean actually results in deterioration of salinity there. The EnOI scheme is found to provide a practical and cost‐effective alternative to the use of an ensemble of forecasts.

This paper presents a description of the CESM/DART ensemble coupled data assimilation (DA) system based on the Community Earth System Model (CESM) and the Data Assimilation Research Testbed (DART) assimilation software. The CESM/DART should be viewed as a flexible system to support the DA needs of the CESM research community and not as a static reanalysis product.In this implementation of the CESM/DART, conventional insitu observations of the ocean and atmosphere are assimilated into the respective component models of the CESM using a 30‐member ensemble adjustment Kalman filter (EAKF). CESM/DART is run in a “weakly coupled” configuration wherein observations native to each climate system component only directly impact the state vector for that component. Information is passed between components indirectly through the short‐term coupled model forecasts that provide the EAKF background ensemble. This system leverages previous ensemble DA development for the Community Atmosphere Model and Parallel Ocean Program models using the DART EAKF. The CESM/DART project is a step towards providing increasingly useful DA capabilities for the CESM research community.Results are presented for our prototype 12‐year reanalysis, run from 1970 to mid 1982. Multiple lines of evidence demonstrate that the system is capable of constraining the CESM coupled model to simulate the historical variability of the climate system in the well‐observed Northern Hemisphere. A collection of monthly average variables, climate mode indices, observation diagnostics and snapshots of synoptic weather in the ocean and atmosphere are compared to established datasets, showing especially good agreement in the Northern Hemisphere. A discussion of the CESM/DART as a modular, community facility and the benefits and challenges associated with this vision is also included.

Simulating Arctic sea ice conditions up to the present and predicting them several months in advance has high stakeholder value, yet remains challenging. Advanced data assimilation (DA) methods combine real observations with model forecasts to produce sea ice reanalyses and accurate initial conditions for sea ice prediction. This study introduces a sea ice DA framework for a sea ice model with a parameterization of the ice thickness distribution by resolving multiple thickness categories. Specifically, the Los Alamos Sea Ice Model, version 5 (CICE5), is integrated with the Data Assimilation Research Testbed (DART). A series of perfect model observing system simulation experiments (OSSEs) are designed to explore DA algorithms within the ensemble Kalman filter (EnKF) and the relative importance of different observation types. This study demonstrates that assimilating sea ice concentration (SIC) observations can effectively remove SIC errors, with the error of total Arctic sea ice area reduced by about 60% annually. When the impact of SIC observations is strongly localized in space, the error of total volume is also modestly improved. The largest simulation improvements are produced when sea ice thickness (SIT) and SIC are jointly assimilated, with the error of total volume decreased by more than 70% annually. Assimilating multiyear sea ice concentration (MYI) can reduce error in total volume by more than 50%. Assimilating MYI produces modest improvements in snow depth (errors are reduced by around 16%), while assimilating SIC and SIT has no obvious influence on snow depth. This study also suggests that different observation types may need different localization distances to optimize DA performance.