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An observing system simulation experiment (OSSE) is a modeling experiment used to evaluate the value of a new observing system. when actual observational data are not available. An OSSE system [...]

Predicting the temporal and spatial patterns of South Asian monsoon rainfall within a season is of critical importance due to its impact on agriculture, water availability, and flooding. The monsoon intraseasonal oscillation (MISO) is a robust northwardpropagating mode that determines the active and break phases of the monsoon and much of the regional distribution of rainfall. However, dynamical atmospheric forecast models predict this mode poorly. Data-driven methods for MISO prediction have shown more skill, but only predict the portion of the rainfall corresponding to MISO rather than the full rainfall signal. Here, we combine state-of-the-art ensemble precipitation forecasts from a high-resolution atmospheric model with data-driven forecasts of MISO. The ensemble members of the detailed atmospheric model are projected onto a lower-dimensional subspace corresponding to the MISO dynamics and are then weighted according to their distance from the data-drivenMISOforecast in this subspace. We thereby achieve improvements in rainfall forecasts over India, as well as the broader monsoon region, at 10-to 30-d lead times, an interval that is generally considered to be a predictability gap. The temporal correlation of rainfall forecasts is improved by up to 0.28 in this time range. Our results demonstrate the potential of leveraging the predictability of intraseasonal oscillations to improve extended-range forecasts; more generally, they point toward a future of combining dynamical and data-driven forecasts for Earth system prediction. [ABSTRACT FROM AUTHOR]

Satellite observations are indispensable for weather forecasting, climate change monitoring, and environmental studies. Understanding and quantifying errors and uncertainties associated with satellite observations are essential for hardware calibration, data assimilation, and developing environmental and climate data records. Satellite observation errors can be classified into four categories: measurement, observation operator, representativeness, and preprocessing errors. Current methods for diagnosing observation errors still yield large uncertainties due to these complex errors. When simulating satellite errors, empirical errors are usually used, which do not always accurately represent the truth. We address these challenges by developing an error inventory simulator, the Satellite Error Representation and Realization (SatERR). SatERR can simulate a wide range of observation errors, from instrument measurement errors to model assimilation errors. Most of these errors are based on physical models, including existing and newly developed algorithms. SatERR takes a bottom-up approach: errors are generated from root sources and forward propagate through radiance and science products. This is different from, but complementary to, the top-down approach of current diagnostics, which inversely solves unknown errors. The impact of different errors can be quantified and partitioned, and a ground-truth testbed can be produced to test and refine diagnostic methods. SatERR is a community error inventory, open-source on GitHub, which can be expanded and refined with input from engineers, scientists, and modelers. This debut version of SatERR is centered on microwave sensors, covering traditional large satellites and small satellites operated by NOAA, NASA, and EUMETSAT. [ABSTRACT FROM AUTHOR]

Ensemble Forecast Sensitivity to Observation (EFSO) is a technique that can efficiently identify the beneficial/detrimental impacts of every observation in ensemble‐based data assimilation (DA). While EFSO has been successfully employed on atmospheric DA, it has never been applied to ocean or coupled DA due to the lack of a suitable error norm for oceanic variables. This study introduces a new density‐based error norm incorporating sea temperature and salinity forecast errors, making EFSO applicable to ocean DA for the first time. We implemented the oceanic EFSO on the CFSv2‐LETKF and investigated the impact of ocean observations under a weakly coupled DA framework. By removing the detrimental ocean observations detected by EFSO, the CFSv2 forecasts were significantly improved, showing the validation of impact estimation and the great potential of EFSO to be extended as a data selection criterion. Plain Language Summary: This study introduces a new method to efficiently estimate the beneficial/detrimental impacts of each oceanic observation in the air‐sea coupled data assimilation (CDA) by incorporating the forecast errors for sea temperature and salinity with a novel density norm. For the first time, we implemented this approach onto the operational‐like CDA system and examined the impacts of ocean observations on the National Centers for Environmental Prediction CFSv2 forecasts. Our findings demonstrate that assimilating ocean profiles and satellite‐retrieved sea surface temperature products significantly improves the CFSv2 forecasts. Moreover, after excluding the detrimental ocean observations identified by our new method, we observed significant improvements in the CFSv2 forecasts. This result validates the accuracy of impact estimation and underscores its great potential as a data selection criterion in CDA. Key Points: A new oceanic Ensemble Forecast Sensitivity to Observation (EFSO) is proposed and implemented on the CFSv2 to investigate observation impactsThe 24‐hr CFSv2 forecasts of sea temperature, salinity, and 2‐m air temperature were improved by 1.5%, 0.8%, and 0.4%, respectively, after removing detrimental ocean observations detected by EFSOThe improvements in the CFSv2 forecast can last for at least 5 days for the ocean and 3 days for the low‐level atmosphere [ABSTRACT FROM AUTHOR]

These models range from stylized mathematical models [[4]], system dynamics models [[5]], and agent-based models [[6]], to complex integrated assessment models (IAMs) [[7]] and coupled component models [[8]] such as the Earth system models [[9]]. The Earth is a very large and complex system that consists of human and natural components interacting bidirectionally with each other, thus forming coupled human and natural systems (CHANS) [[1]]. B Advances in understanding CHANS. b The increasing research efforts on CHANS at multiple scales [[11]], including theoretical, empirical, and modeling studies, enhance understanding of CHANS components, system behaviors, and feedback mechanisms. [Extracted from the article]

Atmospheric inversion of high spatiotemporal surface CO2 fllux without dynamic constraints and sufficient observations is an ill-posed problem, and a priori flux from a "bottom-up" estimation is commonly used in "top-down" inversion systems for regularization purposes. Ensemble Kalman filter-based inversion algorithms usually weigh a priori flux to the background or directly replace the background with the a priori flux. However, the "bottom-up" flux estimations, especially the simulated terrestrial-atmosphere CO2 exchange, are usually systematically biased at different spatiotemporal scales because of the deficiencies in understanding of some underlying processes. Here, we introduced a novel regularization algorithm into the Carbon in Ocean--Land--Atmosphere (COLA) data assimilation system, which assimilates a priori information as a unique observation (AAPO). The a priori information is not limited to "bottom-up" flux estimation. With the comprehensive assimilation regularization approach, COLA can apply the spatial gradient of the "bottom-up" flux estimation as a priori information to reduce the bias impact and enhance the dynamic information concerning the a priori "bottom-up" flux estimation. Benefiting from the enhanced signal-to-noise ratio in the spatial gradient, the global, regional, and grided flux estimations using the AAPO algorithm are significantly better than those obtained by the traditional regularization approach, especially over highly uncertain tropical regions in the context of observing simulation system experiments (OSSEs). We suggest that the AAPO algorithm can be applied to other greenhouse gas (e.g., CH4, NO2) and pollutant data assimilation studies. [ABSTRACT FROM AUTHOR]

The increase in amplitude of the atmospheric carbon dioxide cycle over the past fifty years can be attributed in part to the intensification of agriculture in the Northern Hemisphere. Agricultural advance increases atmospheric CO.sub.2 seasonality The atmospheric CO.sub.2 record displays a seasonal cycle reflecting seasonal variations in CO.sub.2 uptake by terrestrial vegetation. An increase in the amplitude of this seasonal cycle over the past five decades cannot be fully explained at present. Two groups now report that the intensification of agriculture may have been a key contributor to the increase in atmospheric CO.sub.2 seasonal amplitude. Ning Zeng et al. used the VEGAS terrestrial biosphere model to show that enhanced mid-latitude agricultural productivity contributed 45% of the increasing amplitude of global net surface carbon fluxes for the period 1961 to 2010, compared to 29% from climate change and 26% from CO.sub.2 fertilization. Josh Gray et al. used crop production statistics from the UN Food and Agriculture Organization and a carbon accounting model to demonstrate that as much as a quarter of the observed change in atmospheric CO.sub.2 seasonality can be explained by elevated crop productivity, with maize, wheat, rice and soybean major contributors. These studies will contribute to a better understanding of the global carbon cycle, and highlight the extent to which human actions are changing large-scale biosphere-atmosphere interactions. The atmospheric carbon dioxide (CO.sub.2) record displays a prominent seasonal cycle that arises mainly from changes in vegetation growth and the corresponding CO.sub.2 uptake during the boreal spring and summer growing seasons and CO.sub.2 release during the autumn and winter seasons.sup.1,2,3,4. The CO.sub.2 seasonal amplitude has increased over the past five decades, suggesting an increase in Northern Hemisphere biospheric activity.sup.2,5,6. It has been proposed that vegetation growth may have been stimulated by higher concentrations of CO.sub.2 as well as by warming in recent decades, but such mechanisms have been unable to explain the full range and magnitude of the observed increase in CO.sub.2 seasonal amplitude.sup.2,6,7,8,9,10,11,12,13. Here we suggest that the intensification of agriculture (the Green Revolution, in which much greater crop yield per unit area was achieved by hybridization, irrigation and fertilization) during the past five decades is a driver of changes in the seasonal characteristics of the global carbon cycle. Our analysis of CO.sub.2 data and atmospheric inversions shows a robust 15 per cent long-term increase in CO.sub.2 seasonal amplitude from 1961 to 2010, punctuated by large decadal and interannual variations. Using a terrestrial carbon cycle model that takes into account high-yield cultivars, fertilizer use and irrigation, we find that the long-term increase in CO.sub.2 seasonal amplitude arises from two major regions: the mid-latitude cropland between 25° N and 60° N and the high-latitude natural vegetation between 50° N and 70° N. The long-term trend of seasonal amplitude increase is 0.311 [plus or minus] 0.027 per cent per year, of which sensitivity experiments attribute 45, 29 and 26 per cent to land-use change, climate variability and change, and increased productivity due to CO.sub.2 fertilization, respectively. Vegetation growth was earlier by one to two weeks, as measured by the mid-point of vegetation carbon uptake, and took up 0.5 petagrams more carbon in July, the height of the growing season, during 2001-2010 than in 1961-1970, suggesting that human land use and management contribute to seasonal changes in the CO.sub.2 exchange between the biosphere and the atmosphere., Author(s): Ning Zeng [sup.1] , Fang Zhao [sup.1] , George J. Collatz [sup.2] , Eugenia Kalnay [sup.1] , Ross J. Salawitch [sup.1] , Tristram O. West [sup.3] , Luis Guanter [...]

We assessed different coupled data assimilation strategies with a hierarchy of coupled models, ranging from the simple coupled Lorenz model to the state-of-the-art coupled general circulation model CFSv2. With the coupled Lorenz model, we assessed the analysis accuracy by strongly-coupled Ensemble Kalman Filter (EnKF) and 4D-Variational (4D-Var) methods with varying assimilation window lengths. The analysis accuracy of the strongly-coupled EnKF with a short assimilation window is comparable to that of 4D-Var with a long assimilation window. For 4D-Var, the strongly-coupled approach with the coupled model produces more accurate ocean analysis than the ECCO-like approach using the uncoupled ocean model. Experiments with the coupled quasi-geostrophic model conclude that the strongly-coupled approach outperforms the weakly-coupled and uncoupled approaches for both the full-rank EnKF and 4D-Var, with the strongly-coupled EnKF and 4D-Var showing a similar level of accuracy higher than other coupled data assimilation approaches such as the outer loop coupling. A strongly-coupled EnKF software framework is developed and applied to the intermediatecomplexity coupled model SPEEDY-NEMO and the state-of-the-art operational coupled model CFSv2. Experiments assimilating synthetic or real atmospheric observations into the ocean through strongly-coupled EnKF show that the strongly-coupled approach improves the analysis of the atmosphere and upper oceans, but degrades observation fits in the deep ocean, probably due to the unreliable error correlation estimated by a small ensemble. The correlation-cutoff method is developed to reduce the unreliable error correlations between physically irrelevant model states and observations. Experiments with the coupled Lorenz model demonstrate that strongly-coupled EnKF informed by the correlation-cutoff method produces more accurate coupled analyses than the weakly-coupled and plain strongly-coupled EnKF regardless of the ensemble size. To extend the correlation-cutoff method to operational coupled models, a neural network approach is proposed to systematically acquire the observation localization functions for all pairs between the model state and observation types. The following strongly-coupled EnKF experiments with an intermediate-complexity coupled model show promising results with this method. [ABSTRACT FROM AUTHOR]

Atmospheric inversion of carbon dioxide (CO 2) measurements to better understand carbon sources and sinks has made great progress over the last 2 decades. However, most of the studies, including a four-dimensional variational ensemble Kalman filter and Bayesian synthesis approaches, directly obtain only fluxes, while CO 2 concentration is derived with the forward model as part of a post-analysis. Kang et al. (2012) used the local ensemble transform Kalman filter (LETKF), which updates the CO 2 , surface carbon flux (SCF), and meteorology fields simultaneously. Following this track, a system with a short assimilation window and a long observation window was developed (Liu et al., 2019). However, this data assimilation system faces the challenge of maintaining carbon mass conservation. To overcome this shortcoming, here we apply a constrained ensemble Kalman filter (CEnKF) approach to ensure the conservation of global CO 2 mass. After a standard LETKF procedure, an additional assimilation is used to adjust CO 2 at each model grid point and to ensure the consistency between the analysis and the first guess of the global CO 2 mass. Compared to an observing system simulation experiment without mass conservation, the CEnKF significantly reduces the annual global SCF bias from ∼ 0.2 to less than 0.06 Gt and slightly improves the seasonal and annual performance over tropical and southern extratropical regions. We show that this system can accurately track the spatial distribution of annual mean SCF. And the system reduces the seasonal flux root mean square error from a priori to analysis by 48 %–90 %, depending on the continental region. Moreover, the 2015–2016 El Niño impact is well captured with anomalies mainly in the tropics. [ABSTRACT FROM AUTHOR]

Localization is an essential technique for ensemble-based data assimilations (DAs) to reduce sampling errors due to limited ensembles. Unlike traditional distance-dependent localization, the correlation cutoff method (Yoshida and Kalnay, 2018; Yoshida, 2019) tends to localize the observation impacts based on their background error correlations. This method was initially proposed as a variable localization strategy for coupled systems, but it can also can be utilized extensively as a spatial localization. This study introduced and examined the feasibility of the correlation cutoff method as an alternative spatial localization with the local ensemble transform Kalman filter (LETKF) preliminary on the Lorenz (1996) model. We compared the accuracy of the distance-dependent and correlation-dependent localizations and extensively explored the potential of the hybrid localization strategies. Our results suggest that the correlation cutoff method can deliver comparable analysis to the traditional localization more efficiently and with a faster DA spin-up. These benefits would become even more pronounced under a more complicated model, especially when the ensemble and observation sizes are reduced. [ABSTRACT FROM AUTHOR]

The purpose of the present study is to use a new method of empirical model error correction, developed by Danforth et al. in 2007, based on estimating the systematic component of the nonperiodic errors linearly dependent on the anomalous state. The method uses singular value decomposition (SVD) to generate a basis of model errors and states. It requires only a time series of errors to estimate covariances and uses negligible additional computation during a forecast integration. As a result, it should be suitable for operational use at a relatively small computational expense. The method is tested with the Lorenz '96 coupled system as the truth and an uncoupled version of the same system as a model. The authors demonstrate that the SVD method explains a significant component of the effect that the model's unresolved state has on the resolved state and shows that the results are better than those obtained with Leith's empirical correction operator. The improvement is attributed to the fact that the SVD truncation effectively reduces sampling errors. Forecast improvements of up to 1000% are seen when compared with the original model. The improvements come at the expense of weakening ensemble spread.

The National Centers for Environmental Prediction (NCEP), Air Force Weather Agency (AFWA), Fleet Numerical Meteorology and Oceanography Center (FNMOC), National Weather Association, and American Meteorological Society (AMS) cosponsored a 'Symposium on the 50th Anniversary of Operational Numerical Weather Prediction,' on 14-17 June 2004 at the University of Maryland, College Park in College Park, Maryland. Operational numerical weather prediction (NWP) in the United States started with the Joint Numerical Weather Prediction Unit (JNWPU) on 1 July 1954, staffed by members of the U.S. Weather Bureau, the U.S. Air Force and the U.S. Navy. The origins of NCEP, AFWA, and FNMOC can all be traced to the JNWPU. The symposium celebrated the pioneering developments in NWP and the remarkable improvements in forecast skill and support of the nation's economy, well being, and national defense achieved over the last 50 years. This essay was inspired by the presentations from that symposium.