Showing papers by "Eugenia Kalnay published in 2017"

Regional contribution to variability and trends of global gross primary productivity

Abstract: Terrestrial gross primary productivity (GPP) is the largest component of the global carbon cycle and a key process for understanding land ecosystems dynamics. In this study, we used GPP estimates from a combination of eight global biome models participating in the Inter-Sectoral Impact-Model Intercomparison Project phase 2a (ISIMIP2a), the Moderate Resolution Spectroradiometer (MODIS) GPP product, and a data-driven product (Model Tree Ensemble, MTE) to study the spatiotemporal variability of GPP at the regional and global levels. We found the 2000–2010 total global GPP estimated from the model ensemble to be 117 ± 13 Pg C yr−1 (mean ± 1 standard deviation), which was higher than MODIS (112 Pg C yr−1), and close to the MTE (120 Pg C yr−1). The spatial patterns of MODIS, MTE and ISIMIP2a GPP generally agree well, but their temporal trends are different, and the seasonality and inter-annual variability of GPP at the regional and global levels are not completely consistent. For the model ensemble, Tropical Latin America contributes the most to global GPP, Asian regions contribute the most to the global GPP trend, the Northern Hemisphere regions dominate the global GPP seasonal variations, and Oceania is likely the largest contributor to inter-annual variability of global GPP. However, we observed large uncertainties across the eight ISIMIP2a models, which are probably due to the differences in the formulation of underlying photosynthetic processes. The results of this study are useful in understanding the contributions of different regions to global GPP and its spatiotemporal variability, how the model- and observational-based GPP estimates differ from each other in time and space, and the relative strength of the eight models. Our results also highlight the models' ability to capture the seasonality of GPP that are essential for understanding the inter-annual and seasonal variability of GPP as a major component of the carbon cycle.

Assimilating the global satellite mapping of precipitation data with the Nonhydrostatic Icosahedral Atmospheric Model (NICAM)

Abstract: This study aims to propose two new approaches to improve precipitation forecasts from numerical weather prediction (NWP) models through effective data assimilation of satellite-derived precipitation. The assimilation of precipitation data is known to be very difficult mainly because of highly non-Gaussian statistics of precipitation variables. Following Lien et al., this study addresses the non-Gaussianity issue by applying the Gaussian transformation (GT) based on the empirical cumulative distribution function (CDF) of precipitation. We propose a method that constructs the CDF with only recent 1 month samples, without using a long period of samples needed previously. We also propose a method to use the inverse GT, with which we can obtain realistic precipitation fields from biased NWP model outputs. We assimilate the Japan Aerospace eXploration Agency's Global Satellite Mapping of Precipitation (GSMaP) data into the Nonhydrostatic Icosahedral Atmospheric Model (NICAM) at 112 km horizontal resolution. Assimilating the GSMaP data results in improved weather forecasts compared to the control experiment assimilating only rawinsonde data. We find that horizontal observation thinning is necessary, probably due to the horizontal observation-error correlations in the GSMaP data. We also obtained precipitation fields similar to GSMaP from the NICAM precipitation forecasts by using the inverse GT, leading to an improved precipitation forecast.

Inconsistent estimates of forest cover change in China between 2000 and 2013 from multiple datasets: differences in parameters, spatial resolution, and definitions.

Abstract: The Chinese National Forest Inventory (NFI) has reported increased forest coverage in China since 2000, however, the new satellite-based dataset Global Forest Change (GFC) finds decreased forest coverage. In this study, four satellite datasets are used to investigate this discrepancy in forest cover change estimates in China between 2000 and 2013: forest cover change estimated from MODIS Normalized Burn Ratio (NBR), existing MODIS Land Cover (LC) and Vegetation Continuous Fields (VCF) products, and the Landsat-based GFC. Among these satellite datasets, forest loss shows much better agreement in terms of total change area and spatial pattern than do forest gain. The net changes in forest cover as a proportion of China’s land area varied widely from increases of 1.56% in NBR, 1.93% in VCF, and 3.40% in LC to a decline of −0.40% in GFC. The magnitude of net forest increase derived from MODIS datasets (1.56–3.40%) is lower than that reported in NFI (3.41%). Algorithm parameters, different spatial resolutions, and inconsistent forest definitions could be important sources of the discrepancies. Although several MODIS datasets support an overall forest increase in China, the direction and magnitude of net forest change is still unknown due to the large uncertainties in satellite-derived estimates.

The Challenge of Atmospheric Data Assimilation on Mars

Abstract: Data assimilation is carried out for the Martian atmosphere with the Mars Climate Sounder (MCS) retrievals of temperature, dust, and ice. It is performed for the period Ls = 180° to Ls = 320° of Mars Year 29 with the Local Ensemble Transform Kalman Filter scheme and the Laboratoire de Meteorologie Dynamique (LMD) Mars Global Climate Model (GCM). In order to deal with the forcings of aerosols (dust and water ice) on atmospheric temperatures, a framework is given for multivariate analysis. It consists of assimilating a GCM variable with the help of another GCM variable that can be more easily related to an observation. Despite encouraging results with this method, data assimilation is found to be intrinsically different for Mars and more challenging, due to the Martian atmosphere being less chaotic and exhibiting more global features than on Earth. This is reflected in the three main issues met when achieving various data assimilation experiments: (1) temperature assimilation strongly forces the GCM away from its free-running state, due to the difficulty of assimilating global atmospheric thermal tides; (2) because of model bias, assimilation of airborne dust is not able to reproduce the vertical diurnal variations of dust observed by MCS, and not present in the GCM; and (3) water ice clouds are nearly impossible to assimilate due to the difficulty to assimilate temperature to a sufficient precision. Overall, further improvements of Martian data assimilation would require an assimilation that goes beyond the local scale and more realism of the GCM, especially for aerosols and thermal tides.

Proactive QC: A Fully Flow-Dependent Quality Control Scheme Based on EFSO

Abstract: Despite dramatic improvements over the last decades, operational NWP forecasts still occasionally suffer from abrupt drops in their forecast skill. Such forecast skill “dropouts” may occur even in a perfect NWP system because of the stochastic nature of NWP but can also result from flaws in the NWP system. Recent studies have shown that dropouts occur due not to a model’s deficiencies but to misspecified initial conditions, suggesting that they could be mitigated by improving the quality control (QC) system so that the observation-minus-background (O-B) innovations that would degrade a forecast can be detected and rejected. The ensemble forecast sensitivity to observations (EFSO) technique enables for the quantification of how much each observation has improved or degraded the forecast. A recent study has shown that 24-h EFSO can detect detrimental O-B innovations that caused regional forecast skill dropouts and that the forecast can be improved by not assimilating them. Inspired by that success, ...

The pre-Argo ocean reanalyses may be seriously affected by the spatial coverage of moored buoys

Abstract: Assimilation methods, meant to constrain divergence of model trajectory from reality using observations, do not exactly satisfy the physical laws governing the model state variables. This allows mismatches in the analysis in the vicinity of observation locations where the effect of assimilation is most prominent. These mismatches are usually mitigated either by the model dynamics in between the analysis cycles and/or by assimilation at the next analysis cycle. However, if the observations coverage is limited in space, as it was in the ocean before the Argo era, these mechanisms may be insufficient to dampen the mismatches, which we call shocks, and they may remain and grow. Here we show through controlled experiments, using real and simulated observations in two different ocean models and assimilation systems, that such shocks are generated in the ocean at the lateral boundaries of the moored buoy network. They thrive and propagate westward as Rossby waves along these boundaries. However, these shocks are essentially eliminated by the assimilation of near-homogenous global Argo distribution. These findings question the fidelity of ocean reanalysis products in the pre-Argo era. For example, a reanalysis that ignores Argo floats and assimilates only moored buoys, wrongly represents 2008 as a negative Indian Ocean Dipole year.

EFSR: Ensemble Forecast Sensitivity to Observation Error Covariance

Abstract: Data assimilation (DA) methods require an estimate of observation error covariance as an external parameter that typically is tuned in a subjective manner. To facilitate objective and systematic tuning of within the context of ensemble Kalman filtering, this paper introduces a method for estimating how forecast errors would be changed by increasing or decreasing each element of , without a need for the adjoint of the model and the DA system, by combining the adjoint-based -sensitivity diagnostics presented by Daescu previously with the technique employed by Kalnay et al. to derive ensemble forecast sensitivity to observations (EFSO). The proposed method, termed EFSR, is shown to be able to detect and adaptively correct misspecified through a series of toy-model experiments using the Lorenz ’96 model. It is then applied to a quasi-operational global DA system of the National Centers for Environmental Prediction to provide guidance on how to tune the . A sensitivity experiment in which the prescribe...

Finding the driver of local ocean–atmosphere coupling in reanalyses and CMIP5 climate models

Abstract: Identification of the driver of coupled anomalies in the climate system is of great importance for a better understanding of the system and for its use in predictive efforts with climate models. The present analysis examines the robustness of a physical method proposed three decades ago to identify coupled anomalies as of atmospheric or oceanic origin by analyzing 850 mb vorticity and sea surface temperature anomalies. The method is then used as a metric to assess the coupling in climate simulations and a 30-year hindcast from models of the CMIP5 project. Analysis of the frequency of coupled anomalies exceeding one standard deviation from uncoupled NCEP/NCAR and ERA-Interim and partially coupled CFSR reanalyses shows robustness in the main results: anomalies of oceanic origin arise inside the deep tropics and those of atmospheric origin outside of the tropics. Coupled anomalies occupy similar regions in the global oceans independently of the spatiotemporal resolution. Exclusion of phenomena like ENSO, NAO, or AMO has regional effects on the distribution and origin of coupled anomalies; the absence of ENSO decreases anomalies of oceanic origin and favors those of atmospheric origin. Coupled model simulations in general agree with the distribution of anomalies of atmospheric and oceanic origin from reanalyses. However, the lack of the feedback from the atmosphere to the ocean in the AMIP simulations reduces substantially the number of coupled anomalies of atmospheric origin and artificially increases it in the tropics while the number of those of oceanic origin outside the tropics is also augmented. Analysis of a single available 30-year hindcast surprisingly indicates that coupled anomalies are more similar to AMIP than to coupled simulations. Differences in the frequency of coupled anomalies between the AMIP simulations and the uncoupled reanalyses, and similarities between the uncoupled and partially coupled reanalyses, support the notion that the nature of the coupling between the ocean and the atmosphere is transmitted into the reanalyses via the assimilation of observations.

Accelerating assimilation development for new observing systems using EFSO

Abstract: . To successfully assimilate data from a new observing system, it is necessary to develop appropriate data selection strategies, assimilating only the generally useful data. This development work is usually done by trial and error using observing system experiments (OSEs), which are very time and resource consuming. This study proposes a new, efficient methodology to accelerate the development using ensemble forecast sensitivity to observations (EFSO). First, non-cycled assimilation of the new observation data is conducted to compute EFSO diagnostics for each observation within a large sample. Second, the average EFSO conditionally sampled in terms of various factors is computed. Third, potential data selection criteria are designed based on the non-cycled EFSO statistics, and tested in cycled OSEs to verify the actual assimilation impact. The usefulness of this method is demonstrated with the assimilation of satellite precipitation data. It is shown that the EFSO-based method can efficiently suggest data selection criteria that significantly improve the assimilation results.

Estimating Surface Carbon Fluxes Based on a Local Ensemble Transform Kalman Filter with a Short Assimilation Window and a Long Observation Window

Abstract: . We developed a Carbon data assimilation system to estimate the surface carbon fluxes using the Local Ensemble Transform Kalman Filter and atmospheric transfer model of GEOS-Chem driven by the MERRA-1 reanalysis of the meteorological fields based on the Goddard Earth Observing System Model, Version 5 (GEOS-5). This assimilation system is inspired by the method of Kang et al. [2011, 2012], who estimated the surface carbon fluxes in an Observing System Simulation Experiment (OSSE) mode, as evolving parameters in the assimilation of the atmospheric CO2, using a short assimilation window of 6 hours. They included the assimilation of the standard meteorological variables, so that the ensemble provided a measure of the uncertainty in the CO2 transport. After introducing new techniques such as variable localization , and increased observation weights near the surface, they obtained accurate carbon fluxes at grid point resolution. We developed a new version of the LETKF related to the Running-in-Place (RIP) method used to accelerate the spin-up of EnKF data assimilation [Kalnay and Yang, 2010; Wang et al., 2013, Yang et al., 2014]. Like RIP, the new assimilation system uses the no-cost smoothing algorithm for the LETKF [Kalnay et al., 2007b], which allows shifting at no cost the Kalman Filter solution forward or backward within an assimilation window. In the new scheme a long observation window (e.g., 7-days or longer) is used to create an LETKF ensemble at 7-days. Then, the RIP smoother is used to obtain an accurate final analysis at 1-day. This analysis has the advantage of being based on a short assimilation window, which makes it more accurate, and of having been exposed to the future 7-days observations, which accelerates the spin up. The assimilation and observation windows are then shifted forward by one day, and the process is repeated. This reduces significantly the analysis error, suggesting that this method could be used in other data assimilation problems.

Estimating Surface Carbon Fluxes Based on a Local Ensemble Transform Kalman Filter with a Short Assimilation Window and a Long Observation Window

Abstract: . We developed a Carbon data assimilation system to estimate the surface carbon fluxes using the Local Ensemble Transform Kalman Filter and atmospheric transfer model of GEOS-Chem driven by the MERRA-1 reanalysis of the meteorological fields based on the Goddard Earth Observing System Model, Version 5 (GEOS-5). This assimilation system is inspired by the method of Kang et al. (2011, 2012), who estimated the surface carbon fluxes in an Observing System Simulation Experiment (OSSE) mode, as evolving parameters in the assimilation of the atmospheric CO 2 , using a short assimilation window of 6 hours. They included the assimilation of the standard meteorological variables, so that the ensemble provided a measure of the uncertainty in the CO 2 transport. After introducing new techniques such as “variable localization”, and increased observation weights near the surface, they obtained accurate carbon fluxes at grid point resolution. We developed a new version of the LETKF related to the “Running-in-Place” (RIP) method used to accelerate the spin-up of EnKF data assimilation (Kalnay and Yang, 2010; Wang et al., 2013, Yang et al., 2014). Like RIP, the new assimilation system uses the “no-cost smoothing” algorithm for the LETKF (Kalnay et al., 2007b), which allows shifting at no cost the Kalman Filter solution forward or backward within an assimilation window. In the new scheme a long “observation window” (e.g., 7-days or longer) is used to create an LETKF ensemble at 7-days. Then, the RIP smoother is used to obtain an accurate final analysis at 1-day. This analysis has the advantage of being based on a short assimilation window, which makes it more accurate, and of having been exposed to the future 7-days observations, which accelerates the spin up. The assimilation and observation windows are then shifted forward by one day, and the process is repeated. This reduces significantly the analysis error, suggesting that this method could be used in other data assimilation problems. The newly developed assimilation method can be used with other Earth system models, especially for greater use of observations in conjunction with models.

Traveling Weather Systems in the Ensemble Mars Atmosphere Reanalysis System (EMARS)

Abstract: Introduction: The Ensemble Mars Atmosphere Reanalysis System (EMARS) combines insights from spacecraft observations and model simulations using data assimilation, producing a comprehensive, multi-annual record of Martian weather and its uncertainties. Temperature and aerosol retrievals from the Thermal Emission Spectrometer (TES) and Mars Climate Sounder (MCS) instruments are assimilated into the GFDL Mars Global Climate Model (MGCM) using the Local Ensemble Transform Kalman Filter (LETKF) system. The resulting product is a comprehensive gridded dataset of atmospheric properties— temperature, wind, surface pressure, and (dust and water ice cloud) aerosols—and their uncertainties spanning several Mars years. Data assimilation products are a valuable means of synthesizing observations and models to provide insights on Martian weather and climate (Lewis et al., 2007; Hoffman et al., 2010; Montabone et al., 2011; Lee et al., 2011; Greybush et al., 2012; Steele et al., 2014; Navarro et al., 2014).