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Variability in soil moisture has implications for regional terrestrial environments under a warming climate. This paper focuses on the spatiotemporal variability in the intra-annual persistence of soil moisture in China using the fifthgeneration reanalysis dataset by the European Centre for Medium-Range Weather Forecasts for the period 1979-2018. The results show that in China, the mean intra-annual persistence in the humid to arid zones increased from 60 to 115 days in the lower layer but decreased from 19 to 13 days and from 25 to 14 days in the upper and root layers, respectively. However, these changes were strongly attenuated in extremely dry and wet regions due to the scarcity of soil moisture anomalies. Large changes in persistence occurred in the lower soil layer in dryland areas, with a mean difference of up to 40 days between the 2010s and the 1980s. Overall increasing trends dominated the large-scale spatial features, despite regional decreases in the eastern arid zone and the North and Northeast China plains. In the root layer, the two plains experienced an expanded decrease while on the Tibetan Plateau it was dominated by decadal variability. These contrasting changes between the lower and root layers along the periphery of the transition zone was a reflection of the enhanced soil hydrological cycle in the root layer. The enhanced persistence in drylands lower layer is an indication of the intensified impacts of soil moisture anomalies (e.g., droughts) on terrestrial water cycle. These findings may help the understanding of climate change impacts on terrestrial environments. [ABSTRACT FROM AUTHOR]

Apilot coupled climate sensitivity study is presented based on the newly developed adjoint coupled climate model, Centrum für Erdsystemforschung und Nachhaltigkeit (CEN) Earth System Assimilation Model (CESAM). To this end the components of the coupled forward model are summarized, and the generation of the adjoint code out of the model forward code through the application of the Transformation of Algorithms in FORTRAN (TAF) adjoint compiler is discussed. It is shown that simulations of the intermediatecomplexity CESAM are comparable in quality to CMIP-type coupled climate models, justifying the usage of the model to compute adjoint sensitivities of the northern Europe near-surface temperature to anomalies in surface temperature, sea surface salinity, and sea ice over the North Atlantic and the Arctic on time scales of up to one month. Results confirm that on a time scale of up to a few days surface temperatures over northern Europe are influenced by Atlantic temperature anomalies just upstream of the target location. With increasingly longer time lapse, however, it is the influence of SSTs over the central and western North Atlantic on the overlying atmosphere and the associated changes in storm-track pattern that dominate the evolution of the surface European temperature. Influences of surface salinity and sea ice on the northern European temperature appear to have similar sensitivity mechanisms, invoked indirectly through their influence on near-surface temperature anomalies. The adjoint study thus confirms that the SST's impact on the atmospheric dynamics, notably storm tracks, is the primary cause for the influence of northern European temperature changes. [ABSTRACT FROM AUTHOR]

Short-term climate prediction based on a regional climate dynamical model heavily depends on atmospheric forcing and initial soil moisture state. In this study, the Weather Research and Forecasting (WRF) Model with different nudging schemes is used for approximate 2-yr simulations for investigating the importance of soil variables in seasonal temperature and precipitation simulations. The results show that the improvement of seasonal climate simulation (precipitation and air temperature) is more evident in the experiment of assimilating both soil and atmospheric variables than that in the experiments of assimilating atmospheric variables only. Further investigation of the impact of indirectly assimilating soil moisture on precipitation prediction with an indirect soil nudging (ISN) scheme shows that the precipitation reproducibility in summer is better than that in winter, and the effect of ISN is particularly prominent in the region where seasonal precipitation exceeds 200 mm. Moreover, statistical results also illustrate that initial soil moisture plays a crucial role in seasonal precipitation forecasts because of its slowly evolving nature, and its effect is more distinct in semiarid and semihumid regions than in arid and humid regions. The effects of indirectly assimilating soil moisture on precipitation can last two and three months in semiarid and semihumid areas, respectively. [ABSTRACT FROM AUTHOR]

In light of growing interest in data-driven methods for oceanic, atmospheric, and climate sciences, this work focuses on the field of data assimilation and presents the analog data assimilation (AnDA). The proposed framework produces a reconstruction of the system dynamics in a fully data-driven manner where no explicit knowledge of the dynamical model is required. Instead, a representative catalog of trajectories of the system is assumed to be available. Based on this catalog, the analog data assimilation combines the nonparametric sampling of the dynamics using analog forecasting methods with ensemble-based assimilation techniques. This study explores different analog forecasting strategies and derives both ensemble Kalman and particle filtering versions of the proposed analog data assimilation approach. Numerical experiments are examined for two chaotic dynamical systems: the Lorenz-63 and Lorenz-96 systems. The performance of the analog data assimilation is discussed with respect to classical model-driven assimilation. A Matlab toolbox and Python library of the AnDA are provided to help further research building upon the present findings. [ABSTRACT FROM AUTHOR]

The Center for Weather Forecast and Climate Studies [Centro de Previsão e Tempo e Estudos Climáticos (CPTEC)] at the Brazilian National Institute for Space Research [Instituto Nacional de Pesquisas Espaciais (INPE)] has recently operationally implemented a three-dimensional variational data assimilation (3DVAR) scheme based on the Gridpoint Statistical Interpolation analysis system (GSI). Implementation of the GSI system within the atmospheric global circulation model from CPTEC/INPE (AGCM-CPTEC/INPE) is hereafter referred to as the Global 3DVAR (G3DVAR) system. The results of an observing system experiment (OSE) measuring the impacts of radiosonde, satellite radiance, and GPS radio occultation (RO) data on the new G3DVAR system are presented here. The observational impact of each of these platforms was evaluated by measuring the degradation of the geopotential height anomaly correlation and the amplification of the RMSE of the wind. Losing the radiosonde, GPS RO, and satellite radiance data in the OSE resulted in negative impacts on the geopotential height anomaly correlations globally. Nevertheless, the strongest impacts were found over the Southern Hemisphere and South America when satellite radiance data were withheld from the data assimilation system. [ABSTRACT FROM AUTHOR]

Estimated values of recent oceanic heat uptake are on the order of a few tenths of a W m−2, and are a very small residual of air-sea exchanges, with annual average regional magnitudes of hundreds of W m−2. Using a dynamically consistent state estimate, the redistribution of heat within the ocean is calculated over a 20-yr period. The 20-yr mean vertical heat flux shows strong variations in both the lateral and vertical directions, consistent with the ocean being a dynamically active and spatially complex heat exchanger. Between mixing and advection, the two processes determining the vertical heat transport in the deep ocean, advection plays a more important role in setting the spatial patterns of vertical heat exchange and its temporal variations. The global integral of vertical heat flux shows an upward heat transport in the deep ocean, suggesting a cooling trend in the deep ocean. These results support an inference that the near-surface thermal properties of the ocean are a consequence, at least in part, of internal redistributions of heat, some of which must reflect water that has undergone long trajectories since last exposure to the atmosphere. The small residual heat exchange with the atmosphere today is unlikely to represent the interaction with an ocean that was in thermal equilibrium at the start of global warming. An analogy is drawn with carbon-14 'reservoir ages,' which range from over hundreds to a thousand years. [ABSTRACT FROM AUTHOR]

The assimilation of observations in reanalyses incurs the potential for the physical terms of budgets to be balanced by a term relating the fit of the observations relative to a forecast first guess analysis. This may indicate a limitation in the physical processes of the background model or perhaps assimilating data from an inconsistent observing system. In the MERRA reanalysis, an area of long-term moisture flux divergence over land has been identified over the central United States. Here, the water vapor budget is evaluated in this region, taking advantage of two unique features of the MERRA diagnostic output: 1) a closed water budget that includes the analysis increment and 2) a gridded diagnostic output dataset of the assimilated observations and their innovations (e.g., forecast departures). In the central United States, an anomaly occurs where the analysis adds water to the region, while precipitation decreases and moisture flux divergence increases. This is related more to a change in the observing system than to a deficiency in the model physical processes. MERRA's Gridded Innovations and Observations (GIO) data narrow the observations that influence this feature to the ATOVS and Aqua satellites during the 0600 and 1800 UTC analysis cycles, when radiosonde information is not prevalent. Observing system experiments further narrow the instruments that affect the anomalous feature to AMSU-A (mainly window channels) and Atmospheric Infrared Sounder (AIRS). This effort also shows the complexities of the observing system and the reactions of the regional water budgets in reanalyses to the assimilated observations. [ABSTRACT FROM AUTHOR]

Sea ice variability in the Labrador Sea is of climatic interest because of its relationship to deep convection, mode-water formation, and the North Atlantic atmospheric circulation. Historically, quantifying the relationship between sea ice and ocean variability has been difficult because of in situ observation paucity and technical challenges associated with synthesizing observations with numerical models. Here the relationship between ice and ocean variability is explored by analyzing new estimates of the ocean-ice state in the northwest North Atlantic. The estimates are syntheses of in situ and satellite hydrographic and ice data with a regional ⅓° coupled ocean-sea ice model. The synthesis of sea ice data is achieved with an improved adjoint of a thermodynamic ice model. Model and data are made consistent, in a least squares sense, by iteratively adjusting control variables, including ocean initial and lateral boundary conditions and the atmospheric state, to minimize an uncertainty-weighted model-data misfit cost function. The utility of the state estimate is demonstrated in an analysis of energy and buoyancy budgets in the marginal ice zone (MIZ). In mid-March the system achieves a state of quasi-equilibrium during which net ice growth and melt approaches zero; newly formed ice diverges from coastal areas and converges via wind and ocean forcing in the MIZ. The convergence of ice mass in the MIZ is ablated primarily by turbulent ocean-ice enthalpy fluxes. The primary source of the enthalpy required for sustained MIZ ice ablation is the sensible heat reservoir of the subtropical-origin subsurface waters. [ABSTRACT FROM AUTHOR]

Ocean heat content (HC) is one of the key indicators of climate variability and also provides ocean memory critical for seasonal and decadal predictions. The availability of multiple operational ocean analyses (ORAs) now routinely produced around the world is an opportunity for estimation of uncertainties in HC analysis and development of ensemble-based operational HC climate indices. In this context, the spread across the ORAs is used to quantify uncertainties in HC analysis and the ensemble mean of ORAs to identify, and to monitor, climate signals. Toward this goal, this study analyzed 10 ORAs, two objective analyses based on in situ data only, and eight model analyses based on ocean data assimilation systems. The mean, annual cycle, interannual variability, and long-term trend of HC in the upper 300 m (HC300) from 1980 to 2009 are compared. The spread across HC300 analyses generally decreased with time and reached a minimum in the early 2000s when the Argo data became available. There was a good correspondence between the increase of data counts and reduction of the spread. The agreement of HC300 anomalies among different ORAs, measured by the signal-to-noise ratio (S/N), is generally high in the tropical Pacific, tropical Indian Ocean, North Pacific, and North Atlantic but low in the tropical Atlantic and extratropical southern oceans where observations are very sparse. A set of climate indices was derived as HC300 anomalies averaged over the areas where the covariability between SST and HC300 represents the major climate modes such as ENSO, Indian Ocean dipole, Atlantic Niño, Pacific decadal oscillation, and Atlantic multidecadal oscillation. [ABSTRACT FROM AUTHOR]